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url: http://arxiv.org/abs/1908.04832v1
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[1908.04832v1] Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System

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# Computer Science > Computation and Language

**arXiv:1908.04832v1** (cs) 

[Submitted on 13 Aug 2019]

# Title:Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System

Authors:[Kevin K. Bowden](https://arxiv.org/search/cs?searchtype=author&query=Bowden,+K+K), [Jiaqi Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+J), [Wen Cui](https://arxiv.org/search/cs?searchtype=author&query=Cui,+W), [Juraj Juraska](https://arxiv.org/search/cs?searchtype=author&query=Juraska,+J), [Vrindavan Harrison](https://arxiv.org/search/cs?searchtype=author&query=Harrison,+V), [Brian Schwarzmann](https://arxiv.org/search/cs?searchtype=author&query=Schwarzmann,+B), [Nicholas Santer](https://arxiv.org/search/cs?searchtype=author&query=Santer,+N), [Steve Whittaker](https://arxiv.org/search/cs?searchtype=author&query=Whittaker,+S), [Marilyn Walker](https://arxiv.org/search/cs?searchtype=author&query=Walker,+M)

View a PDF of the paper titled Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System, by Kevin K. Bowden and 8 other authors

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> Abstract:Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this. 

Comments: | To appear in 1st International Conference on Conversational User Interfaces (CUI 2019)  
---|---  
Subjects: |  Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)  
Cite as: | [arXiv:1908.04832](https://arxiv.org/abs/1908.04832) [cs.CL]  
  | (or  [arXiv:1908.04832v1](https://arxiv.org/abs/1908.04832v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.1908.04832> Focus to learn more arXiv-issued DOI via DataCite  
Related DOI:  | <https://doi.org/10.1145/3342775.3342792> Focus to learn more DOI(s) linking to related resources   
  
## Submission history

From: Kevin Bowden [[view email](/show-email/375ef9a6/1908.04832)]   
**[v1]** Tue, 13 Aug 2019 19:10:36 UTC (169 KB)  


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url: http://arxiv.org/abs/2407.20382v1
title: [2407.20382v1] What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models
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# Computer Science > Computation and Language

**arXiv:2407.20382v1** (cs) 

[Submitted on 29 Jul 2024]

# Title:What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models

Authors:[Navapat Nananukul](https://arxiv.org/search/cs?searchtype=author&query=Nananukul,+N), [Wichayaporn Wongkamjan](https://arxiv.org/search/cs?searchtype=author&query=Wongkamjan,+W)

View a PDF of the paper titled What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models, by Navapat Nananukul and 1 other authors

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> Abstract:Role-playing games (RPGs) provide players with a rich, interactive world to explore. Dialogue serves as the primary means of communication between developers and players, manifesting in various forms such as guides, NPC interactions, and storytelling. While most games rely on written scripts to define the main story and character personalities, player immersion can be significantly enhanced through casual interactions between characters. With the advent of large language models (LLMs), we introduce a dialogue filler framework that utilizes LLMs enhanced by knowledge graphs to generate dynamic and contextually appropriate character interactions. We test this framework within the environments of Final Fantasy VII Remake and Pokemon, providing qualitative and quantitative evidence that demonstrates GPT-4's capability to act with defined personalities and generate dialogue. However, some flaws remain, such as GPT-4 being overly positive or more subtle personalities, such as maturity, tend to be of lower quality compared to more overt traits like timidity. This study aims to assist developers in crafting more nuanced filler dialogues, thereby enriching player immersion and enhancing the overall RPG experience. 

Comments: | ACL Wordplay 2024  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:2407.20382](https://arxiv.org/abs/2407.20382) [cs.CL]  
  | (or  [arXiv:2407.20382v1](https://arxiv.org/abs/2407.20382v1) [cs.CL] for this version)   
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## Submission history

From: Wichayaporn Wongkamjan [[view email](/show-email/617ff2e6/2407.20382)]   
**[v1]** Mon, 29 Jul 2024 19:12:18 UTC (9,833 KB)  


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url: http://arxiv.org/abs/2411.04671v3
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hash: ba1cf0cd1f176ddc52b7177e642d11c7ab2ba2265f5a5b5de9d7166c6c4cc131
title:
[2411.04671v3] CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR

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# Computer Science > Human-Computer Interaction

**arXiv:2411.04671v3** (cs) 

[Submitted on 7 Nov 2024 ([v1](https://arxiv.org/abs/2411.04671v1)), last revised 3 Mar 2025 (this version, v3)]

# Title:CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR

Authors:[Kadir Burak Buldu](https://arxiv.org/search/cs?searchtype=author&query=Buldu,+K+B), [Süleyman Özdel](https://arxiv.org/search/cs?searchtype=author&query=%C3%96zdel,+S), [Ka Hei Carrie Lau](https://arxiv.org/search/cs?searchtype=author&query=Lau,+K+H+C), [Mengdi Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+M), [Daniel Saad](https://arxiv.org/search/cs?searchtype=author&query=Saad,+D), [Sofie Schönborn](https://arxiv.org/search/cs?searchtype=author&query=Sch%C3%B6nborn,+S), [Auxane Boch](https://arxiv.org/search/cs?searchtype=author&query=Boch,+A), [Enkelejda Kasneci](https://arxiv.org/search/cs?searchtype=author&query=Kasneci,+E), [Efe Bozkir](https://arxiv.org/search/cs?searchtype=author&query=Bozkir,+E)

View a PDF of the paper titled CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR, by Kadir Burak Buldu and S\"uleyman \"Ozdel and Ka Hei Carrie Lau and Mengdi Wang and Daniel Saad and Sofie Sch\"onborn and Auxane Boch and Enkelejda Kasneci and Efe Bozkir

[View PDF](/pdf/2411.04671v3) [HTML (experimental)](https://arxiv.org/html/2411.04671v3)

> Abstract:Recent developments in computer graphics, machine learning, and sensor technologies enable numerous opportunities for extended reality (XR) setups for everyday life, from skills training to entertainment. With large corporations offering affordable consumer-grade head-mounted displays (HMDs), XR will likely become pervasive, and HMDs will develop as personal devices like smartphones and tablets. However, having intelligent spaces and naturalistic interactions in XR is as important as technological advances so that users grow their engagement in virtual and augmented spaces. To this end, large language model (LLM)--powered non-player characters (NPCs) with speech-to-text (STT) and text-to-speech (TTS) models bring significant advantages over conventional or pre-scripted NPCs for facilitating more natural conversational user interfaces (CUIs) in XR. This paper provides the community with an open-source, customizable, extendable, and privacy-aware Unity package, CUIfy, that facilitates speech-based NPC-user interaction with widely used LLMs, STT, and TTS models. Our package also supports multiple LLM-powered NPCs per environment and minimizes latency between different computational models through streaming to achieve usable interactions between users and NPCs. We publish our source code in the following repository: [this https URL](https://gitlab.lrz.de/hctl/cuify)

Comments: | 7th IEEE International Conference on Artificial Intelligence & eXtended and Virtual Reality (IEEE AIxVR 2025)  
---|---  
Subjects: |  Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:2411.04671](https://arxiv.org/abs/2411.04671) [cs.HC]  
  | (or  [arXiv:2411.04671v3](https://arxiv.org/abs/2411.04671v3) [cs.HC] for this version)   
  |  <https://doi.org/10.48550/arXiv.2411.04671> Focus to learn more arXiv-issued DOI via DataCite  
Related DOI:  | <https://doi.org/10.1109/AIxVR63409.2025.00037> Focus to learn more DOI(s) linking to related resources   
  
## Submission history

From: Efe Bozkir [[view email](/show-email/35a751ee/2411.04671)]   
**[[v1]](/abs/2411.04671v1)** Thu, 7 Nov 2024 12:55:17 UTC (274 KB)  
**[[v2]](/abs/2411.04671v2)** Thu, 27 Feb 2025 11:26:20 UTC (334 KB)  
**[v3]** Mon, 3 Mar 2025 13:41:33 UTC (334 KB)  


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url: http://arxiv.org/abs/2312.14480v1
scrape_date: 2025-03-11T15:39:25.504112
content_type: markdown
hash: ab82969ec9777c48f188e7bf4b465756219d2291c88e549d6979249417aa8b11
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[2312.14480v1] MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models

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# Computer Science > Cryptography and Security

**arXiv:2312.14480v1** (cs) 

[Submitted on 22 Dec 2023]

# Title:MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models

Authors:[Hongyin Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+H)

View a PDF of the paper titled MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models, by Hongyin Zhu

[View PDF](/pdf/2312.14480v1) [HTML (experimental)](https://arxiv.org/html/2312.14480v1)

> Abstract:Large language models (LLMs) are increasingly being used in Metaverse environments to generate dynamic and realistic content and to control the behavior of non-player characters (NPCs). However, the cybersecurity concerns associated with LLMs have become increasingly prominent. Previous research has primarily focused on patching system vulnerabilities to enhance cybersecurity, but these approaches are not well-suited to the Metaverse, where the virtual space is more complex, LLMs are vulnerable, and ethical user interaction is critical. Moreover, the scope of cybersecurity in the Metaverse is expected to expand significantly. This paper proposes a method for enhancing cybersecurity through the simulation of user interaction with LLMs. Our goal is to educate users and strengthen their defense capabilities through exposure to a comprehensive simulation system. This system includes extensive Metaverse cybersecurity Q&A and attack simulation scenarios. By engaging with these, users will improve their ability to recognize and withstand risks. Additionally, to address the ethical implications of user input, we propose using LLMs as evaluators to assess user content across five dimensions. We further adapt the models through vocabulary expansion training to better understand personalized inputs and emoticons. We conduct experiments on multiple LLMs and find that our approach is effective. 

Subjects: |  Cryptography and Security (cs.CR); Computation and Language (cs.CL); Computers and Society (cs.CY)  
---|---  
Cite as: | [arXiv:2312.14480](https://arxiv.org/abs/2312.14480) [cs.CR]  
  | (or  [arXiv:2312.14480v1](https://arxiv.org/abs/2312.14480v1) [cs.CR] for this version)   
  |  <https://doi.org/10.48550/arXiv.2312.14480> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Hongyin Zhu [[view email](/show-email/ac0c6c27/2312.14480)]   
**[v1]** Fri, 22 Dec 2023 07:15:55 UTC (396 KB)  


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url: https://arxiv.org/abs/2402.09742
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content_type: markdown
hash: 371eb0174a86d884362cba32daa89cd1d95f14deacbd1803f9a0e9611fb6847c
title:
[2402.09742] AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator

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# Computer Science > Computation and Language

**arXiv:2402.09742** (cs) 

[Submitted on 15 Feb 2024 ([v1](https://arxiv.org/abs/2402.09742v1)), last revised 28 Jun 2024 (this version, v4)]

# Title:AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator

Authors:[Zhihao Fan](https://arxiv.org/search/cs?searchtype=author&query=Fan,+Z), [Jialong Tang](https://arxiv.org/search/cs?searchtype=author&query=Tang,+J), [Wei Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+W), [Siyuan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+S), [Zhongyu Wei](https://arxiv.org/search/cs?searchtype=author&query=Wei,+Z), [Jun Xi](https://arxiv.org/search/cs?searchtype=author&query=Xi,+J), [Fei Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+F), [Jingren Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+J)

View a PDF of the paper titled AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator, by Zhihao Fan and 7 other authors

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> Abstract:Artificial intelligence has significantly advanced healthcare, particularly through large language models (LLMs) that excel in medical question answering benchmarks. However, their real-world clinical application remains limited due to the complexities of doctor-patient interactions. To address this, we introduce \textbf{AI Hospital}, a multi-agent framework simulating dynamic medical interactions between \emph{Doctor} as player and NPCs including \emph{Patient}, \emph{Examiner}, \emph{Chief Physician}. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation (MVME) benchmark, utilizing high-quality Chinese medical records and NPCs to evaluate LLMs' performance in symptom collection, examination recommendations, and diagnoses. Additionally, a dispute resolution collaborative mechanism is proposed to enhance diagnostic accuracy through iterative discussions. Despite improvements, current LLMs exhibit significant performance gaps in multi-turn interactions compared to one-step approaches. Our findings highlight the need for further research to bridge these gaps and improve LLMs' clinical diagnostic capabilities. Our data, code, and experimental results are all open-sourced at \url{[this https URL](https://github.com/LibertFan/AI_Hospital)}. 

Comments: | [this https URL](https://github.com/LibertFan/AI_Hospital)  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:2402.09742](https://arxiv.org/abs/2402.09742) [cs.CL]  
  | (or  [arXiv:2402.09742v4](https://arxiv.org/abs/2402.09742v4) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2402.09742> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Zhihao Fan [[view email](/show-email/0546694b/2402.09742)]   
**[[v1]](/abs/2402.09742v1)** Thu, 15 Feb 2024 06:46:48 UTC (9,452 KB)  
**[[v2]](/abs/2402.09742v2)** Wed, 21 Feb 2024 08:25:25 UTC (9,452 KB)  
**[[v3]](/abs/2402.09742v3)** Thu, 27 Jun 2024 15:40:53 UTC (2,883 KB)  
**[v4]** Fri, 28 Jun 2024 03:11:48 UTC (2,883 KB)  


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url: https://arxiv.org/html/2404.18231v1
title: From Persona to Personalization: A Survey on Role-Playing Language Agents
scrape_date: 2025-03-11T15:39:25.759002
content_type: markdown
hash: c6a1a07293845c6d97ab32f55e25c0c3dfe2af90fc2d51e2219d42953bb95d93
content:
  1. [1 Introduction](https://arxiv.org/html/2404.18231v1#S1 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
  2. [2 Preliminary](https://arxiv.org/html/2404.18231v1#S2 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [2.1 The Roadmap of Large Language Models](https://arxiv.org/html/2404.18231v1#S2.SS1 "In 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Emerged Abilities in LLMs](https://arxiv.org/html/2404.18231v1#S2.SS1.SSS0.Px1 "In 2.1 The Roadmap of Large Language Models ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Anthropomorphic Cognition in LLMs](https://arxiv.org/html/2404.18231v1#S2.SS1.SSS0.Px2 "In 2.1 The Roadmap of Large Language Models ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Retrieval-augmented Generation of LLMs](https://arxiv.org/html/2404.18231v1#S2.SS1.SSS0.Px3 "In 2.1 The Roadmap of Large Language Models ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [2.2 LLM-powered Language Agents](https://arxiv.org/html/2404.18231v1#S2.SS2 "In 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Planning Module](https://arxiv.org/html/2404.18231v1#S2.SS2.SSS0.Px1 "In 2.2 LLM-powered Language Agents ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Tool-usage Module](https://arxiv.org/html/2404.18231v1#S2.SS2.SSS0.Px2 "In 2.2 LLM-powered Language Agents ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Memory Mechanism](https://arxiv.org/html/2404.18231v1#S2.SS2.SSS0.Px3 "In 2.2 LLM-powered Language Agents ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  3. [3 Overview of RPLAs](https://arxiv.org/html/2404.18231v1#S3 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [3.1 RPLA Definition](https://arxiv.org/html/2404.18231v1#S3.SS1 "In 3 Overview of RPLAs ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [3.2 RPLA Construction](https://arxiv.org/html/2404.18231v1#S3.SS2 "In 3 Overview of RPLAs ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    3. [3.3 RPLA Evaluation](https://arxiv.org/html/2404.18231v1#S3.SS3 "In 3 Overview of RPLAs ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  4. [4 Demographic Persona](https://arxiv.org/html/2404.18231v1#S4 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [4.1 Definition](https://arxiv.org/html/2404.18231v1#S4.SS1 "In 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [4.2 Analysis of Demographics](https://arxiv.org/html/2404.18231v1#S4.SS2 "In 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Inherent Demographics](https://arxiv.org/html/2404.18231v1#S4.SS2.SSS0.Px1 "In 4.2 Analysis of Demographics ‣ 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Demographic Role-Playing](https://arxiv.org/html/2404.18231v1#S4.SS2.SSS0.Px2 "In 4.2 Analysis of Demographics ‣ 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    3. [4.3 Application of Demographics](https://arxiv.org/html/2404.18231v1#S4.SS3 "In 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Improving Task Solving in Single-Agent Systems](https://arxiv.org/html/2404.18231v1#S4.SS3.SSS0.Px1 "In 4.3 Application of Demographics ‣ 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Improving Task Solving in Multi-Agent Systems](https://arxiv.org/html/2404.18231v1#S4.SS3.SSS0.Px2 "In 4.3 Application of Demographics ‣ 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Simulating Collective Social Behaviors in Multi-Agent Systems](https://arxiv.org/html/2404.18231v1#S4.SS3.SSS0.Px3 "In 4.3 Application of Demographics ‣ 4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  5. [5 Character Persona](https://arxiv.org/html/2404.18231v1#S5 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [5.1 Definition](https://arxiv.org/html/2404.18231v1#S5.SS1 "In 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [5.2 Data for Character RPLAs](https://arxiv.org/html/2404.18231v1#S5.SS2 "In 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    3. [5.3 Construction of Character RPLAs](https://arxiv.org/html/2404.18231v1#S5.SS3 "In 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Parametric Training](https://arxiv.org/html/2404.18231v1#S5.SS3.SSS0.Px1 "In 5.3 Construction of Character RPLAs ‣ 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Nonparametric Prompting](https://arxiv.org/html/2404.18231v1#S5.SS3.SSS0.Px2 "In 5.3 Construction of Character RPLAs ‣ 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    4. [5.4 Evaluation of Character RPLAs](https://arxiv.org/html/2404.18231v1#S5.SS4 "In 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Character-independent Capabilities](https://arxiv.org/html/2404.18231v1#S5.SS4.SSS0.Px1 "In 5.4 Evaluation of Character RPLAs ‣ 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Character Fidelities](https://arxiv.org/html/2404.18231v1#S5.SS4.SSS0.Px2 "In 5.4 Evaluation of Character RPLAs ‣ 5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  6. [6 Individualized Persona(lization)](https://arxiv.org/html/2404.18231v1#S6 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [6.1 Definition](https://arxiv.org/html/2404.18231v1#S6.SS1 "In 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [6.2 Data Collection of Individualized Persona](https://arxiv.org/html/2404.18231v1#S6.SS2 "In 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Profiles](https://arxiv.org/html/2404.18231v1#S6.SS2.SSS0.Px1 "In 6.2 Data Collection of Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Interactions](https://arxiv.org/html/2404.18231v1#S6.SS2.SSS0.Px2 "In 6.2 Data Collection of Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Domain Knowledge](https://arxiv.org/html/2404.18231v1#S6.SS2.SSS0.Px3 "In 6.2 Data Collection of Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    3. [6.3 Modeling Individualized Persona](https://arxiv.org/html/2404.18231v1#S6.SS3 "In 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Offline Learning](https://arxiv.org/html/2404.18231v1#S6.SS3.SSS0.Px1 "In 6.3 Modeling Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Online Learning](https://arxiv.org/html/2404.18231v1#S6.SS3.SSS0.Px2 "In 6.3 Modeling Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    4. [6.4 Evaluation for LLMs and Individualized Persona](https://arxiv.org/html/2404.18231v1#S6.SS4 "In 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Conversation](https://arxiv.org/html/2404.18231v1#S6.SS4.SSS0.Px1 "In 6.4 Evaluation for LLMs and Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Recommendation](https://arxiv.org/html/2404.18231v1#S6.SS4.SSS0.Px2 "In 6.4 Evaluation for LLMs and Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Task Solving](https://arxiv.org/html/2404.18231v1#S6.SS4.SSS0.Px3 "In 6.4 Evaluation for LLMs and Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  7. [7 Risks Beneath RPLA Applications](https://arxiv.org/html/2404.18231v1#S7 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [7.1 Toxicity](https://arxiv.org/html/2404.18231v1#S7.SS1 "In 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Inherent Toxicity in LLMs](https://arxiv.org/html/2404.18231v1#S7.SS1.SSS0.Px1 "In 7.1 Toxicity ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [The RPLAs Conundrum](https://arxiv.org/html/2404.18231v1#S7.SS1.SSS0.Px2 "In 7.1 Toxicity ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Strategies for Balancing Safety and Performance](https://arxiv.org/html/2404.18231v1#S7.SS1.SSS0.Px3 "In 7.1 Toxicity ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [7.2 Bias](https://arxiv.org/html/2404.18231v1#S7.SS2 "In 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Bias Manifestation in Role-Playing Scenarios](https://arxiv.org/html/2404.18231v1#S7.SS2.SSS0.Px1 "In 7.2 Bias ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Causes of Bias in RPLAs](https://arxiv.org/html/2404.18231v1#S7.SS2.SSS0.Px2 "In 7.2 Bias ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Strategies for Mitigating Bias](https://arxiv.org/html/2404.18231v1#S7.SS2.SSS0.Px3 "In 7.2 Bias ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      4. [Persona Construction Bias](https://arxiv.org/html/2404.18231v1#S7.SS2.SSS0.Px4 "In 7.2 Bias ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    3. [7.3 Hallucination](https://arxiv.org/html/2404.18231v1#S7.SS3 "In 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Hallucination in RPLAs](https://arxiv.org/html/2404.18231v1#S7.SS3.SSS0.Px1 "In 7.3 Hallucination ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Mitigating Hallucinations in RPLAs](https://arxiv.org/html/2404.18231v1#S7.SS3.SSS0.Px2 "In 7.3 Hallucination ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    4. [7.4 Privacy Violations](https://arxiv.org/html/2404.18231v1#S7.SS4 "In 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Privacy Challenges in LLMs](https://arxiv.org/html/2404.18231v1#S7.SS4.SSS0.Px1 "In 7.4 Privacy Violations ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Hidden Danger of Privacy Violations in RPLAs](https://arxiv.org/html/2404.18231v1#S7.SS4.SSS0.Px2 "In 7.4 Privacy Violations ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Strategies for Enhancing Privacy](https://arxiv.org/html/2404.18231v1#S7.SS4.SSS0.Px3 "In 7.4 Privacy Violations ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    5. [7.5 Technical Challenges in Real-world Deployment](https://arxiv.org/html/2404.18231v1#S7.SS5 "In 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Lack of Social Intelligence and Theory of Mind](https://arxiv.org/html/2404.18231v1#S7.SS5.SSS0.Px1 "In 7.5 Technical Challenges in Real-world Deployment ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Long-context Challenges](https://arxiv.org/html/2404.18231v1#S7.SS5.SSS0.Px2 "In 7.5 Technical Challenges in Real-world Deployment ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Knowledge Gaps](https://arxiv.org/html/2404.18231v1#S7.SS5.SSS0.Px3 "In 7.5 Technical Challenges in Real-world Deployment ‣ 7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  8. [8 Closing Remarks](https://arxiv.org/html/2404.18231v1#S8 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [Future Directions on RPLA Systems](https://arxiv.org/html/2404.18231v1#S8.SS0.SSS0.Px1 "In 8 Closing Remarks ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
  9. [A Products](https://arxiv.org/html/2404.18231v1#A1 "In From Persona to Personalization: A Survey on Role-Playing Language Agents")
    1. [A.1 Persona-oriented RPLA Products](https://arxiv.org/html/2404.18231v1#A1.SS1 "In Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Interactions between Humans and RPLAs](https://arxiv.org/html/2404.18231v1#A1.SS1.SSS0.Px1 "In A.1 Persona-oriented RPLA Products ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Interactions among RPLAs](https://arxiv.org/html/2404.18231v1#A1.SS1.SSS0.Px2 "In A.1 Persona-oriented RPLA Products ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
    2. [A.2 Task-oriented RPLAs](https://arxiv.org/html/2404.18231v1#A1.SS2 "In Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      1. [Education](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px1 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      2. [Human Resource](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px2 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      3. [Real Estate](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px3 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      4. [Content Generation](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px4 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      5. [Health](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px5 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      6. [Travel](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px6 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      7. [Customer Service](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px7 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      8. [Shopping](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px8 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      9. [Fitness](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px9 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")
      10. [Office](https://arxiv.org/html/2404.18231v1#A1.SS2.SSS0.Px10 "In A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents")



# From Persona to Personalization:   
A Survey on Role-Playing Language Agents

Jiangjie Chen∗1, Xintao Wang∗1, Rui Xu∗1, Siyu Yuan∗1, Yikai Zhang∗1, Wei Shi∗1, Jian Xie∗1 Shuang Li1, Ruihan Yang1, Tinghui Zhu1, Aili Chen1, Nianqi Li1, Lida Chen1, Caiyu Hu2, Siye Wu3, Scott Ren4, Ziquan Fu5, Yanghua Xiao1 ∗indicates equal contribution. 1Fudan University   
2Shanghai University 3Wuhan University 4UC Santa Barbara 5System, Inc. 

###### Abstract

Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, including in-context learning, instruction following, and social intelligence, RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. RPLAs can mimic a wide range of personas, ranging from historical figures and fictional characters to real-life individuals. Consequently, they have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots, and digital clones. In this paper, we conduct a comprehensive survey of this field, illustrating the evolution and recent progress in RPLAs integrating with cutting-edge LLM technologies. We categorize personas into three types:  1) Demographic Persona, which leverages statistical stereotypes;  2) Character Persona, focused on well-established figures; and  3) Individualized Persona, customized through ongoing user interactions for personalized services.  We begin by presenting a comprehensive overview of current methodologies for RPLAs, followed by the details for each persona type, covering corresponding data sourcing, agent construction, and evaluation. Afterward, we discuss the fundamental risks, existing limitations, and future prospects of RPLAs. Additionally, we provide a brief review of RPLAs in AI applications, which reflects practical user demands that shape and drive RPLA research. Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.

††Correspondence to: {jjchen19, shawyh}@fudan.edu.cn, {xtwang21,ruixu21}@m.fudan.edu.cn.

##  1 Introduction

Digital life has been a pursuit for humanity for decades, reflecting our deep-rooted fascination with the intersection of technology and human experience. Bridging this pursuit with imaginative concepts, role-playing AI systems embody the digital life by bringing these personas to life in interactive forms. These systems, which simulate assigned personas, have long been a concept in the human imagination, capturing the essence of our desire to create and interact with artificial beings that can understand, respond, and engage with us in a seemingly sentient manner. With role-playing agents, various personas can be replicated by their agent counterparts, including historical figures, fictional characters, or individuals in our daily lives. Recently, focusing on the text modality, Role-Playing Language Agents (RPLAs) are coming into reality (Shanahan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib187); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)), which inspires a wide range of novel applications, such as digital clones for individuals (Ng et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib154)), AI characters in chatbots (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), and role-playing video games (Wang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib208)), even stimulating social science research (Rao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib173)). As RPLAs become increasingly integrated into our daily lives, it is essential to foster a society that thrives on the synergistic coexistence of humans and these intelligent agents.

Recent developments in Large Language Models (LLMs) (OpenAI, [2023](https://arxiv.org/html/2404.18231v1#bib.bib156); Google, [2023](https://arxiv.org/html/2404.18231v1#bib.bib63); Anthropic, [2024](https://arxiv.org/html/2404.18231v1#bib.bib9)) have greatly facilitated the emergence of RPLAs. LLMs grow adept at producing a compelling sense of human likeness (Shanahan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib187); Zhou et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib279)), and can be regarded as superpositions of beliefs (Kovač et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib107)) and personas (Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)). Furthermore, with alignment training, LLMs are able to adhere to the instruction of persona role-playing, including replicating their knowledge (Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)), linguistic and behavior patterns (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)), and even underlying personalities (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)). They are able to both mimic the personas as prompted in the contexts (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)), or harness their inherent parametric knowledge for widely-recognized demographics or characters (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)). Considering their practical significance, there has been an increase in research efforts dedicated to RPLAs with LLMs, including their development (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)), analysis (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Yuan et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib258)), and applications (Rao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib173); Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162); Mysore et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib151)). Conversely, RPLAs also offer an ideal perspective and testing ground for investigating the behaviors and capabilities of LLMs and language agents, particularly those related to social interactions (Li et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib116); Chen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib33)).

![Refer to caption](x1.png) Figure 1: An overview of various persona types for RPLAs. In this survey, we categorize personas into three types: 1) Demographic Persona, 2) Character Persona, and 3) Individualized Persona. We showcase their definition, data sources, examples, use cases and corresponding applications.

In this paper, we conduct a comprehensive survey on RPLAs. Our study primarily focuses on the persona and personalization of RPLAs. Specifically, as shown in Figure [1](https://arxiv.org/html/2404.18231v1#S1.F1 "Figure 1 ‣ 1 Introduction ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), we categorize personas within RPLA literature at three levels, with a progressive integration of personalized data:

  1. 1.

Demographic Persona, i.e., focusing on groups of people sharing common characteristics, such as occupations, ethnic groups, personality types, etc. These personas are inherent in LLMs, and role-playing them capitalizes on the statistical stereotypes in LLMs (Huang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib82); Xu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib238); Gupta et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib70)).

  2. 2.

Character Persona, which represents well-established and widely-recognized individuals, especially in the existing literature, including celebrities, historical figures, and fictional characters. Role-playing these personas challenges models’ capability in understanding curated materials of the existing characters, harnessing knowledge in LLMs’ parameters or given contexts (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)).

  3. 3.

Individualized Persona, referring to digital profiles built and continuously updated based on personalized user data. This category emphasizes the unique experiences, needs, and preferences of individuals, aiming for applications such as digital clones or personal assistance (Salemi et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib178); Woźniak et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib230)). RPLAs for these personas underscore their dynamic nature and learning mechanism and frequently focus on interactions with real-world activities (Dalvi Mishra et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib48); Chen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib34); Salemi et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib178)).




The three types of personas exhibit a progressive relationship and can coexist in RPLAs. For example, an RPLA portraying Socrates as a personal philosophy tutor would encompass the demographic persona of an ancient Greek philosopher, the character persona of Socrates, and an individualized persona that develops through interactions with the user. Following this categorization, we explore common methodologies, fundamental risks, current gaps and limitations, and future prospects of RPLAs in this survey.

In summary, this survey systematically reviews existing literature in the field of RPLAs, and establishes taxonomies for relevant methodologies as shown in Figure [2](https://arxiv.org/html/2404.18231v1#S2.F2 "Figure 2 ‣ Memory Mechanism ‣ 2.2 LLM-powered Language Agents ‣ 2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"). The remainder of our paper is structured as follows: §§\mathsection§[2](https://arxiv.org/html/2404.18231v1#S2 "2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") introduces the background for RPLAs, covering the roadmap, recent progress, and trends in LLMs and language agents. §§\mathsection§[3](https://arxiv.org/html/2404.18231v1#S3 "3 Overview of RPLAs ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") then presents the overview of current research in RPLAs. §§\mathsection§[4](https://arxiv.org/html/2404.18231v1#S4 "4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"),[5](https://arxiv.org/html/2404.18231v1#S5 "5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"),[6](https://arxiv.org/html/2404.18231v1#S6 "6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") detail the research on RPLAs for demographic, character, and individualized persona, respectively. §§\mathsection§[7](https://arxiv.org/html/2404.18231v1#S7 "7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") discusses potential risks of RPLAs, such as toxicity, biases and misuse. Finally, §§\mathsection§[8](https://arxiv.org/html/2404.18231v1#S8 "8 Closing Remarks ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") concludes this survey and identifies future directions. Additionally, aiming to bridge the gap between theoretical insights and practical applications for our readers, we also conduct a brief survey of current RPLA products in the rapidly growing market in Appendix [A](https://arxiv.org/html/2404.18231v1#A1 "Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents").

##  2 Preliminary

###  2.1 The Roadmap of Large Language Models

Recently, LLMs have demonstrated impressive capabilities, with promising potential in approaching human-level intelligence (Brown et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib26); OpenAI, [2022](https://arxiv.org/html/2404.18231v1#bib.bib155); Anil et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib6); Anthropic, [2023a](https://arxiv.org/html/2404.18231v1#bib.bib7); [b](https://arxiv.org/html/2404.18231v1#bib.bib8); OpenAI, [2023](https://arxiv.org/html/2404.18231v1#bib.bib156)). LLMs are artificial neural networks with billions of parameters, trained on vast amounts of natural language data representing human knowledge and intelligence. Their accomplishments extend beyond excelling in NLP tasks to effectively simulating a broader range of human behaviors. Specifically, they have showcased more nuanced capabilities towards anthropomorphic cognition, including humanity emulation (Shanahan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib187); Huang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib82)) and social intelligence (Kosinski, [2023](https://arxiv.org/html/2404.18231v1#bib.bib105); Li et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib116); Kim et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib101)), thus producing a compelling sense of human likeness. As a result, advancements in LLMs have significantly facilitated the creation of intelligent RPLAs (Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162); Sclar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib185); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), establishing new effective methodologies different from previous models.

#### Emerged Abilities in LLMs

Several key abilities have emerged in LLMs (Wei et al., [2022a](https://arxiv.org/html/2404.18231v1#bib.bib224)) throughout their evolution, including in-context learning (Brown et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib26)), instruction following (Ouyang et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib157)), step-by-step reasoning (Wei et al., [2022b](https://arxiv.org/html/2404.18231v1#bib.bib225)), and social intelligence (Wang et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib213); Sclar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib185); Light et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib128)), which lay the foundation for complicated role-playing behavior of LLMs towards RPLAs. First, the in-context learning ability allows LLMs to learn information from prompts without parameter updates. This facilitates LLMs’ adaptation to the provided knowledge of various characters and mimicking their behaviors by following example demonstrations. Second, the instruction following ability enables LLMs to adhere to role-playing instructions, such as “Serve as a helpful assistant” or “Role-play Hermione Granger in the Harry Potter Series. <Description>. <Example Conversations>. <Requirements>.”. Finally, step-by-step reasoning and social intelligence refine LLMs in terms of anthropomorphic cognition, contributing to an enriched sense of human likeness and nuanced emotional support in RPLA applications.

#### Anthropomorphic Cognition in LLMs

Recent research has showcased the emergence of many human-like traits in LLMs (Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162); [2022](https://arxiv.org/html/2404.18231v1#bib.bib161)). Initially, LaMDA (Cohen et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib43)) sparked the first discussion that consciousness might have emerged in language models. Since then, there has been growing research focus on human-like traits in LLMs, including self-awareness (Li et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib125); Blum & Blum, [2023](https://arxiv.org/html/2404.18231v1#bib.bib21)), values (Scherrer et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib182); Hartmann et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib75)), emotional perception (Huang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib80); Lee et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib110)), psychopathy (Coda-Forno et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib42); Li et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib123)) and personalities (Huang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib82); Miotto et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib146)). Shanahan et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib187)) attributes such humanity emulation to the role-playing nature of LLMs, i.e., generating text that resembles human dialogue, which should not be regarded as an indication of consciousness.

#### Retrieval-augmented Generation of LLMs

Retrieval-augmented generation (RAG) recently gains popularity as a method to enhance the capability of LLMs by integrating external data retrieval into the generative process (Karpukhin et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib97); Lewis et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib111); Alon et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib4); Ma et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib142); Berchansky et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib19); Jiang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib94)). By dynamically retrieving information from knowledge bases during the inference phase, RAG greatly mitigates the generation of factually incorrect content (Borgeaud et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib22); Cheng et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib39); Dai et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib47); Kim et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib99)), thereby making RAG an effective method in the role-playing scenarios (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)). Moreover, with the extension of context length in recent research (Wang et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib215); Li et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib114); Liu et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib135); Ding et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib52); Chen et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib36); Han et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib73); Packer et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib158); Liu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib133); Su et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib199)), LLMs have unlocked new potentials for role-playing, being able to understand novels and documents without retrieval mechanism that fragments persona information.

###  2.2 LLM-powered Language Agents

The AI community has long been pursuing the concept of “agent”, approaching the intelligence and autonomy of humans. Traditional symbolic agents (Bernstein, [2001](https://arxiv.org/html/2404.18231v1#bib.bib20); Küngas et al., [2004](https://arxiv.org/html/2404.18231v1#bib.bib109)) and reinforce-learning agents (Fachantidis et al., [2017](https://arxiv.org/html/2404.18231v1#bib.bib56); Florensa et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib60)) mainly optimize their actions based on rules or pre-defined rewards. Research in language agents primarily focuses on training within constrained environments with limited knowledge, diverging from the complex and diverse nature of the human learning process. However, such agents struggle to emulate complicated human-like behaviors, particularly in open-domain settings (Mnih et al., [2015](https://arxiv.org/html/2404.18231v1#bib.bib147); Lillicrap et al., [2015](https://arxiv.org/html/2404.18231v1#bib.bib129); Schulman et al., [2017](https://arxiv.org/html/2404.18231v1#bib.bib184); Haarnoja et al., [2017](https://arxiv.org/html/2404.18231v1#bib.bib72)). Recently, LLMs have demonstrated remarkable capabilities with promising potential in achieving human-level intelligence, which has sparked a rise in research focusing on LLM-based language agents (Sclar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib185); Chalamalasetti et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib28); Liu et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib137); Xie et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib236)). Research in this area primarily involves equipping LLMs with essential human-like capabilities, such as planning, tool-usage and memory (Weng, [2023](https://arxiv.org/html/2404.18231v1#bib.bib228)), which are essential for developing advanced RPLAs with anthropomorphic cognition and abilities.

#### Planning Module

In many real-world scenarios, the agents need to make long-horizon planning to solve complex tasks (Rana et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib172); Yuan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib256)). When facing these tasks, LLM-powered agents could decompose the complex tasks into subtasks and adopt various planning strategies, e.g., CoT (Wei et al., [2022b](https://arxiv.org/html/2404.18231v1#bib.bib225)) and ReAct (Yao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib250)), to adaptively plan for the next action with feedback from environments (Wang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib208); Gotts et al., [2003](https://arxiv.org/html/2404.18231v1#bib.bib65); Wang et al., [2023j](https://arxiv.org/html/2404.18231v1#bib.bib223); Song et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib196); Zhang et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib266)). For RPLAs, these adaptive planning strategies enable them to simulate realistic and dynamic interactions in complicated environments such as games (Wang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib208)) and social simulations (Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162)).

#### Tool-usage Module

Although LLMs excel in various tasks, they may struggle in domains requiring extensive expertise and experience hallucination issues (Gou et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib66); Chen et al., [2023e](https://arxiv.org/html/2404.18231v1#bib.bib37); Wang et al., [2023f](https://arxiv.org/html/2404.18231v1#bib.bib217)). To address these challenges, agents could apply external tools for action execution (Shen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib192); Lu et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib140); Schick et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib183); Parisi et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib160); Yang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib247); Yuan et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib257)). The tools include real-world APIs (Patil et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib163); Li et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib121); Qin et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib171); Xu et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib241); Shen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib193)), knowledge bases (Zhuang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib283); Hsieh et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib78)), external models (Bran et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib24); Ruan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib175)), and customized actions for specific applications (Wang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib208); Zhu et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib282)). For RPLAs, these tools typically enable them to interact with the environments, e.g., games or software applications. The integration of external tools enhances role-playing and generative agents by enabling them to execute actions and access information beyond their intrinsic capabilities. This facilitates more accurate and contextually appropriate interactions, particularly in specialized or complex scenarios, thereby significantly improving the quality and effectiveness of their responses in user engagements.

#### Memory Mechanism

The memory mechanism stores the profile of agents along with environmental information to assist agents in future actions. The profile typically includes basic information (age, gender, career), psychological traits (reflecting personality), and social relationships (Wang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib212); Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162); Qian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib169)), which can be manually created (Caron & Srivastava, [2022](https://arxiv.org/html/2404.18231v1#bib.bib27); Zhang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib260); Pan & Zeng, [2023](https://arxiv.org/html/2404.18231v1#bib.bib159); Huang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib81); Karra et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib98); Safdari et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib177)) or generated from models (Wang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib212)). This module enables agents to accumulate experiences, evolve, and act consistently and effectively (Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162)). Language agents draw from cognitive science research on human memory, which progresses from sensory to short-term, then to long-term memory (Atkinson & Shiffrin, [1968](https://arxiv.org/html/2404.18231v1#bib.bib11); Craik & Jennings, [1992](https://arxiv.org/html/2404.18231v1#bib.bib44)). The short-term memory is regarded as the information input within the constraint window of transformer architecture (Fischer, [2023](https://arxiv.org/html/2404.18231v1#bib.bib59); Rana et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib172); Wang et al., [2023j](https://arxiv.org/html/2404.18231v1#bib.bib223); Zhu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib281)). In contrast, long-term memory is usually reserved in the external vector storage (Qian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib169); Zhong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib274); Zhu et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib282); Lin et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib131); Xie et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib235); Wu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib233)) or natural languages database (Shinn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib194); Modarressi et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib148)), from which agents can quickly query and retrieve information as required. Compared to vanilla LLMs, language agents need to learn and perform tasks in changing environments. For RPLAs, the memory mechanism plays a pivotal role by enabling these agents to maintain continuity and context in interactions over time. By storing and retrieving user-specific data and environmental context, agents deliver more personalized and relevant responses, thus enhancing user experience and engagement in diverse scenarios.

{forest}

for tree= forked edges, grow’=0, calign=child edge, calign child=(n_children()+1)/2, , [RPLAs, root [Demographic Persona §[4](https://arxiv.org/html/2404.18231v1#S4 "4 Demographic Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), demographic [Analysis, demographic [Inherent Demographics, demographic [Serapio-García et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib186)); MPI; PsychoBench; OpinionQA; Hartmann et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib75)), demographic_work] ] [Demographic Role-playing, demographic [Chain Prompting; Serapio-García et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib186)); Deshpande et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib49)); PAIR, demographic_work] ] ] [Application, demographic [Task Sovling, demographic [ MetaGPT; OKR-AGENT, demographic_work ] ] [Social Behaviors, demographic [ Cicero;  The Avalon;  The Werewolf ;  Humaniod Agent , demographic_work ] ] ] ] [Character Persona §[5](https://arxiv.org/html/2404.18231v1#S5 "5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), character [Data Source, character [ChatHaruhi; CharacterGLM; Character-LLM; RoleLLM; HPD; CharacterEval; DITTO; NarrativePlay; MORTISE, character_work] ] [Construction, character [Parametric Training, character [Character-LLM; ChatHaruhi; Neeko; RoleLLM, character_work] ] [Nonparametric Prompting, character [RoleLLM; CharacterGLM; ChatHaruhi; InCharacter; LifeChoice, character_work] ] ] [Evaluation, character [Character-independent Capabilities, character [ Character-LLM;  InCharacter;  CharacterGLM; CharacterEval; Deshpande et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib49)); Kosinski ([2023](https://arxiv.org/html/2404.18231v1#bib.bib105)); Sorin et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib197)); Huang et al. ([2023a](https://arxiv.org/html/2404.18231v1#bib.bib80)); Zhou et al. ([2024b](https://arxiv.org/html/2404.18231v1#bib.bib279)); Wang et al. ([2024a](https://arxiv.org/html/2404.18231v1#bib.bib213)), character_work ] ] [ Linguistic Style and Knowledge, character [RoleLLM;  ChatHaruhi; CharacterGLM; Neeko; HPD; Character-LLM; RoleEval; NarrativePlay; MORTISE; DITTO, character_work] ] [ Personality and Thinking Process, character [Character-LLM; LifeChoice; RoleInteract; InCharacter; Cross-MR, character_work] ] ] ] [Individualized Persona §[6](https://arxiv.org/html/2404.18231v1#S6 "6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), personalization [Data Source, personalization [Crowd-sourcing, personalization [Zhang et al. ([2018](https://arxiv.org/html/2404.18231v1#bib.bib262));  ConvAI2; FoCus; RealPersonaChat; KBP; Cho et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib41)); ECHO, personalization_work] ] [Internet, personalization [P-Ubuntu; MPCHAT; COMSET; LiveChat, personalization_work] ] [LLM, personalization [Synthetic-Persona-Chat, personalization_work] ] ] [Construction, personalization [Offline Learning, personalization [PersonaAware-D2S;  GIRL;  personalized-RLHF; Jang et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib90));  VaRMI;  ControlLM, personalization_work] ] [Online Learning, personalization [TeachMe; Kim et al. ([2024](https://arxiv.org/html/2404.18231v1#bib.bib100)); Baek et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib12)); CoPS;  PEARL;  Persona-DB;  ExploreLLM; Qin et al. ([2024](https://arxiv.org/html/2404.18231v1#bib.bib170));  GeneInput; Bai et al. ([2022](https://arxiv.org/html/2404.18231v1#bib.bib13));ECHO, personalization_work] ] ] [Evaluation, personalization [Conversation, personalization [LiveChat;  FoCus;  Poly-encoders; MPCHAT; PRM; Dinan et al. ([2018](https://arxiv.org/html/2404.18231v1#bib.bib50));ECHO , personalization_work] ] [Recommend-ation, personalization [LFM;  PALR;  IGL;  ONCE;  PEARL; Dai et al. ([2023a](https://arxiv.org/html/2404.18231v1#bib.bib45)); Hou et al. ([2024](https://arxiv.org/html/2404.18231v1#bib.bib77)); Kang et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib96)); Liu et al. ([2023a](https://arxiv.org/html/2404.18231v1#bib.bib134)), personalization_work] ] [Task Solving, personalization [G4C;  MMToM-QA;  OpenToM;  Toolllm;  Api-bank;  Toolqa;  UltraTool;  AutoDroid;  TaskBench;  Assistgui;  Planbench , personalization_work]] ] ] [Risks §[7](https://arxiv.org/html/2404.18231v1#S7 "7 Risks Beneath RPLA Applications ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), risk [Toxicity, risk [PDP; Deshpande et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib49)); MPCHAT,risk_work] ] [Bias, risk [PROMPTINJECT; Branch et al. ([2022](https://arxiv.org/html/2404.18231v1#bib.bib25)); Zheng et al. ([2023a](https://arxiv.org/html/2404.18231v1#bib.bib270)); Kotek et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib106)); Marked Personas; CAMEL; Rutinowski et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib176)); Hartmann et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib75)); Xue et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib244)); Coda-Forno et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib42)), risk_work] ] [Hallucination, risk [ Li et al. ([2016](https://arxiv.org/html/2404.18231v1#bib.bib118)); Zhang et al. ([2018](https://arxiv.org/html/2404.18231v1#bib.bib262)); Neel et al. ([2021](https://arxiv.org/html/2404.18231v1#bib.bib153)); Pawelczyk et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib164)); SPP; Character-LLM, risk_work] ] [Privacy Violations, risk [K-Anonymity; ProPILE; Li et al. ([2023e](https://arxiv.org/html/2404.18231v1#bib.bib117)); Staab et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib198)), risk_work] ] [Technical Challenge in Real-world Deployment, risk[ COSPLAY; FANToM; Character-LLM; Liu et al. ([2023b](https://arxiv.org/html/2404.18231v1#bib.bib135)); Shao et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib188)); CharacterEval, risk_work] ] ] ]

Figure 2: Taxonomy of representative recent research on RPLAs.

##  3 Overview of RPLAs

In this section, we present a concise overview of current research on RPLAs.

###  3.1 RPLA Definition

Our survey distinguishes personas into three categories, progressing from broad groups to individual specificity: demographic persona, character persona, and individualized persona, defined as follows:

  1. 1.

Demographic Persona represents the aggregated characteristics and behaviors of distinct demographic segments, including occupations, genders, ethnicity, and personality types. In the context of RPLAs, these personas operate as fictional archetypes, derived from the comprehensive pre-training datasets of LLMs. Employing these archetypes, the development of RPLAs can be efficiently facilitated through simple prompts, such as “You are a mathematician.” Constructed in this way, demographic RPLAs can be effectively employed for simulations specific to demographic groups and for addressing specialized tasks.

  2. 2.

Character Persona denotes well-established characters, encompassing both real-world public figures and fictional entities, each characterized by definitive attributes and narratives. The RPLAs for these characters are constructed using data derived from diverse sources such as biographies, novels, and films. Primarily, these RPLAs are designed to fulfill entertainment and emotional engagement needs, functioning as AI-driven chatbots or virtual characters in video games.

  3. 3.

Individualized Persona refers to personal profiles constructed from the behavioral and preference data of specific individuals, encompassing personal profiles, dialogues, and a range of actions and behaviors. This data is subject to continuous evolution, necessitating that the corresponding RPLAs adapt dynamically to these changes. Individualized RPLAs provide customized services tailored to the needs of individual users across various AI-based applications, where they commonly function as personalized assistants, companions, or proxies.




###  3.2 RPLA Construction

Role-Playing Language Agents (RPLAs) are primarily developed to simulate intricate personas based on various individual profiles and narratives. These profiles are constructed using diverse persona data, including descriptive narratives, dialogues, historical behaviors, and extensive textual materials such as books (Zhang et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib262); Dinan et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib51); Shanahan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib187); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Xu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib238); Li et al., [2023f](https://arxiv.org/html/2404.18231v1#bib.bib120)).

The methodologies for building RPLAs typically involve either parametric training (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Qin et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib170)) or nonparametric prompting (Dalvi Mishra et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib48); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Gupta et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib70); Ma et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib141); Zhao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib269)), both of which may concurrently contribute to the development process. In parametric training, RPLAs undergo pre-training using large-scale raw text, including literary works and encyclopedic entries (Xu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib238); Gupta et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib70)), succeeded by supervised fine-tuning focused on persona-specific dialogues (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)). Conversely, nonparametric prompting presents role-playing instructions and examples, based on persona descriptions and dialogue instances (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Deshpande et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib49); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)). Furthermore, modern RPLAs increasingly integrate memory modules to retrieve information from extensive datasets on character traits or past interactions. This development addresses the restricted context capacity of current LLMs (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Mysore et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib151); Sun et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib201)).

In terms of alignment with persona types, parametric learning tends to focus on demographic information and well-known characters, whereas prompting techniques are generally employed for generating fictional personas and highly personalized characters. Current research in RPLA development generally focuses on steering LLMs with demographics (Zhang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib261); Hong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib76)), developing foundation models (Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)), designing agent frameworks (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)) for RPLAs, and crafting persona profiles for specified individuals (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)).

###  3.3 RPLA Evaluation

For RPLA evaluation, we distinguish the criteria into two primary categories: role-playing capability evaluation for RPLA methodologies, and persona fidelity evaluation for specific personas (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)). The role-playing capabilities of RPLAs are evaluated on their foundation models and construction frameworks, regardless of specific personas. These evaluations concern aspects such as anthropomorphic abilities, attractiveness, and usefulness, which encompass more granular dimensions including conversation ability (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), engagement (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)) 111By “engagement”, we mean the state of whether the LLMs are successfully engaged in role-playing activities., persona consistency (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)), emotion understanding (Huang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib80)), theory of mind (Kosinski, [2023](https://arxiv.org/html/2404.18231v1#bib.bib105)), and problem-solving ability (Xu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib238)). Persona fidelity, by contrast, concentrates on whether individual RPLAs well replicate the intended personas, including their knowledge (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)), linguistic habits (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Deshpande et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib49)), personality (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219); Huang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib82)), believes (Li et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib113); Wang et al., [2023e](https://arxiv.org/html/2404.18231v1#bib.bib216)), and decision-making (Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242); Chen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib33)). Overall, current RPLAs have demonstrated promising and improving performance in simulating personas. However, there remain significant gaps between existing RPLAs and fully human-level intelligent agents (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Chen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib33)).

##  4 Demographic Persona

###  4.1 Definition

RPLAs assigned with demographic personas are expected to display unique characteristics of specific groups of people. Within this context, demographics capture typical traits associated with groups possessing common characteristics, such as occupational roles (e.g., a mathematician), hobbies or interests (e.g., a baseball enthusiast), and personality types (e.g., the ENFJ category from the Myers-Briggs Type Indicator), etc. These representations in RPLAs meld the linguistic style, professional knowledge, and behavioral nuances representative of a demographic archetype.

These RPLAs are designed to mimic how a specific demographic processes and engages with information and communication channels, reflecting its unique language preferences, domain-specific vocabulary, and distinctive viewpoints. This transformation aims to translate the broad and flexible capabilities of RPLAs into complex virtual representations that reflect the intellectual subtleties, personal inclinations, and social complexities of the demographic. By embodying specific groups, demographic RPLAs can enhance their abilities in certain areas, and also utilize a variety of RPLAs representing different demographics for social experiments, the completion of more complex tasks, etc.

###  4.2 Analysis of Demographics

RPLAs possess inherent demographics that reflect nuanced human-like characteristics, including personality traits, political beliefs, and ethical considerations, which vary in different LLMs. Furthermore, RPLAs have the ability to role-play specified demographics, altering their behavior and potentially enhancing their performance on specific tasks, but this may also lead to toxic outputs and biases, depending on the persona assigned.

#### Inherent Demographics

RPLAs may inherently reflect specific demographic characteristics due to patterns present in the data used during pretraining. These patterns encapsulate human tendencies and biases originating from diverse sources (Karra et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib98); Serapio-García et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib186); Gupta et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib71)). Subsequently, RPLAs could encode individual behavioral traits in textual outputs, inadvertently resulting in a disproportionate emphasis on certain demographics over others (Jiang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib93)).

To harness RPLAs for specific applications effectively, it is essential to understand their inherent demographics. The demographic characteristics of RPLAs can be explored through established human psychological assessments such as the Big Five Personality Test (Barrick & Mount, [1991](https://arxiv.org/html/2404.18231v1#bib.bib16)). By subjecting RPLAs to text-based questionnaires designed for humans, researchers could leverage their textual response capabilities to evaluate behavioral responses similar to human subjects (Huang et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib83)). Such evaluations have revealed that RPLAs exhibit consistent inherent demographics, which have been statistically confirmed in recent studies (Jiang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib93); Serapio-García et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib186); Santurkar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib180)). However, it is important to recognize that these demographics may differ in different LLMs (Huang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib81)).

Beyond personality characteristics, RPLAs often display complex demographics reflecting nuanced social, economic, and ethical understanding. For instance, RPLAs may exhibit a preference for certain political beliefs (Hartmann et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib75)), show decision-making patterns indicative of rational economic considerations (Guo et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib69)), and act either selfishly or helpfully in multi-agent simulations (Chawla et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib31)).

#### Demographic Role-Playing

RPLAs are embedded with intrinsic demographic characteristics, which raises pivotal questions about their ability to role-play specified demographics and the subsequent effects on their behavior. A prevalent approach in demographic role-play involves directly prompting the language agent. For example, if an LLM is prompted with, “You’re a friendly and outgoing individual who thrives on social interactions. Always ready for a good time, you enjoy being the center of attention at parties…” (Jiang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib93)), it adopts the persona of an extroverted character. When tasked with representing distinct demographics, RPLAs demonstrate the capacity to diverge from their inherent traits, manifesting changes in their responses on psychological assessment scales (Jiang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib93); Serapio-García et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib186)). This behavioral adaptability highlights the potential of RPLAs in simulating diverse human-like roles and personalities.

However, not all assigned personas lead to superior performance of RPLAs. Assigning a persona to LLMs may also result in toxic or biased outputs compared to the default setting, because the persona may amplify existing stereotypes and biases present in the training data. For example, the assignment of some personas to language agents, including baseline personas such as “a bad person”, has been demonstrated to significantly increase the likelihood of RPLAs generating toxic outputs (Deshpande et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib49)). Similarly, diverse demographic roles have been assigned to reveal the biased presumptions present in LLMs (Gupta et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib70)). Although some developers have made attempts to prevent RPLAs from malicious usage, attacking the prompts via “jailbreaking” (Chao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib29); [Anil et al., ](https://arxiv.org/html/2404.18231v1#bib.bib5)) might bypass these safety mechanisms and elicit offensive, toxic, misleading contents.

###  4.3 Application of Demographics

By assigning specific demographics, LLMs often have better performance in various types of downstream tasks, whether agents are used in a standalone fashion (single-agent systems) or joint with other agents (multi-agent systems) for competition or collaboration.

#### Improving Task Solving in Single-Agent Systems

Assigning specific demographics enables LLMs to enhance their performance in tasks that require specialized knowledge tied to those personas. For instance, when an LLM is configured to represent an expert LLM within a specific field, it might significantly augment the length, depth, and quality of its responses, which is also showcased in complex zero-shot reasoning tasks, where the model must generate insightful answers without prior direct training on similar problems (Xu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib238); Kong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib104)). Furthermore, integrating diverse social roles into LLMs’ frameworks has been shown to positively influence their performance across a wide array of tasks, suggesting a versatile adaptability to different contextual demands (Zheng et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib270)). The application of these roles enables LLMs not only to generate more contextually appropriate responses but also to exhibit increased understanding and engagement in interactions that reflect varied human experiences and societal norms.

#### Improving Task Solving in Multi-Agent Systems

Building upon the capability of single-agent models, which utilize demographic personas to bolster their specialized abilities, assigning demographic personas in multi-agent systems has also emerged as a crucial strategy for enhancing the performance of single-agent systems, i.e., standalone LLMs. By embedding various personas within agents, distinct societal dynamics could be cultivated, leading to improved strategies for cooperative problem-solving and breakthroughs in complex domains such as mathematical modeling (Zhang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib261); Wang et al., [2023h](https://arxiv.org/html/2404.18231v1#bib.bib221)). A notable implementation of this approach is ChatDev (Qian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib169)) and MetaGPT (Hong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib76)), frameworks designed specifically for automating software development within a multi-agent conversational platform. In this setup, different agents are assigned specialized roles that collectively contribute to the agile development of software applications. This collaborative model echoes the strategies applied in projects such as OKR-AGENT (Zheng et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib271)), where role-specific enhancements within multi-agent architectures have shown to significantly streamline and optimize task execution.

#### Simulating Collective Social Behaviors in Multi-Agent Systems

RPLAs have demonstrated remarkable capabilities in simulating nuanced, human-like interactions across various environments. In the realm of gaming, particularly in strategy and role-playing scenarios, RPLAs have shown impressive performance. For example, Chawla et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib31)) set the agents to be fair or selfish, and shows that selfish agents could contribute not only to their own interests but also to the collective good. Additionally, more elaborate games like Social Deduction Games are particularly illustrative of RPLAs’ capacity to effectively adopt varied roles, as observed in scenarios such as “The Werewolf” (Xu et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib243)) and “The Avalon” (Wang et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib214)). In diplomacy-focused games such as Cicero, RPLAs have matched or even surpassed human levels of performance et al.(2022)(FAIR), Bakhtin, Brown, Dinan, Farina, Flaherty, Fried, Goff, Gray, Hu, et al. ([FAIR](https://arxiv.org/html/2404.18231v1#bib.bib57)). Similarly, in war simulation games, RPLAs provide valuable insights into the origins of conflicts, enhancing our understanding of complex geopolitical dynamics (Hua et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib79)). Extending the application of RPLAs beyond gaming environments, these models are also utilized to mimic daily social interactions, thereby narrowing the behavioral gap between artificial agents and humans. This is exemplified in the development of Humanoid Agent frameworks (Wang et al., [2023i](https://arxiv.org/html/2404.18231v1#bib.bib222)), which embody System 1 functionalities—such as basic needs and emotions—to enhance realism and effectiveness in replicating human responses and behaviors. Furthermore, recent findings in multi-agent interaction environments have revealed that diversifying the types of agents, scaling up their number, and increasing interactions, lead to the emergence of unplanned social behaviors. Such behaviors arise spontaneously from discussions among multiple agents, highlighting the potential for complex, dynamic systems within LLM architectures (Gu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib67)). This progression from specific gaming applications to broader social simulations illustrates the expanding versatility and depth of RPLAs in understanding and replicating human-like behavior.

##  5 Character Persona

###  5.1 Definition

Characters are primarily established individuals with their stories widely recognized by the public, including celebrities, historical figures and fictional characters (e.g., Monkey D. Luffy and Hermione Granger). Occasionally, they also include original characters created by individuals (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)). Character RPLAs have recently emerged as a flourishing field of LLM application (e.g., Character.ai), and hence attracted wide research interest as well (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)).

For character RPLAs, the essential requirement for effective role-playing is the ability of LLMs to understand characters. Early research has studied character understanding of language models, involving linking descriptions that outline characters’ traits to their roles (i.e., Character Prediction) and personalities (i.e., Personality Understanding):  1) Character prediction mainly focuses on recognizing characters from a provided text. This includes tasks like co-reference resolution (Li et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib115)), relationship understanding (Zhao et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib268)) and character identification (Brahman et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib23); Yu et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib253); Li et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib115); Zhao et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib268)). Additionally, some studies investigate if language models can forecast characters’ future actions based on  2) Personality understanding aims to decode character traits from their dialogues and actions, including predicting the depicted traits (Yu et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib254)) and generating character descriptions (Brahman et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib23)). 

In recent years, LLMs have demonstrated strong capabilities in language understanding and generation, which significantly advanced the development of RPLAs. The research focus in this direction has hence shifted towards applying and promoting LLMs to faithfully reproduce the characters, including their linguistic style (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), knowledge (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), personality (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)), and even decision-making (Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267); Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242)).

Table 1: Datasets for depicting characters. #Character represents the number of characters, with each character having a specific description. #Sample indicates the number of samples. A sample refers to a dialogue or question, and * denotes numbers of multi-turn dialogues. Method describes how samples in the datasets are constructed. Experience Extraction extracts characters’ dialogues or scenes from corresponding origins, while Dialogue Synthesis generates role-playing conversations with advanced LLMs.  Papers | #Character | #Samples | Language | Source | Method  
---|---|---|---|---|---  
PDP (Han et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib74)) | 327 | 1,042,647 |  EN ZH | TV shows |  Experience Extraction Dialogue Synthesis  
Character-LLM (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)) | 9 | 1,900,800 | EN | Encyclopedia |  Experience Extraction Dialogue Synthesis  
ChatHaruhi (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)) | 32 | 54,726 |  EN ZH |  Books Games Movies |  Experience Extraction Dialogue Synthesis  
RoleLLM (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)) | 100 | 140,726 |  EN ZH | Scripts |  Experience Extraction Dialogue Synthesis  
HPD (Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35)) | - | 1,191* |  EN ZH | Books |  Dialogue Synthesis Human Annotation  
CharacterGLM (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)) | 250 | 1034* | ZH |  Books Scripts |  Experience Extraction Dialogue Synthesis Human Annotation  
PIPPA (Gosling et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib64)) | 1,254 | 25,940* |  EN |  Character.ai- Users |  Dialogue Synthesis  
RoleEval (Shen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib191)) | 300 | 6,000 |  EN ZH | Encyclopedia |  Dialogue Synthesis  
CharacterEval (Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)) | 77 | 11,376 | ZH |  Books Scripts |  Experience Extraction Human Annotation  
MORTISE (Tang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib205)) | 190 | 17,835* |  EN ZH |  Encyclopedia Other Datasets | Dialogue Synthesis  
CroSS-MR (Yuan et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib258)) | 126 | 445 | EN |  Literature- Analysis |  Experience Extraction  
DITTO (Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)) | 4,002 | 36,662 |  EN ZH | Encyclopedia |  Experience Extraction Dialogue Synthesis  
RoleInteract (Chen et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib32)) | 500 | 30,800 |  EN ZH |  Books Movies |  Experience Extraction Dialogue Synthesis  
LifeChoice (Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242)) | 1,401 | 1,401 | EN |  Literature- Analysis |  Experience Extraction  
InCharacter (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)) | 32 | 18,304 |  EN ZH |  Personality- Tests |  Dialogue Synthesis  
  
###  5.2 Data for Character RPLAs

Character data is indispensable for the construction of character RPLAs. The data that represents knowledge of these well-established characters can be roughly categorized into two types:  1) Descriptionsdirectly describe the character personas that guide the behaviors of RPLAs. These include various character attributes, such as identity, relationships, and other predetermined attributes. The attributes serve as the knowledge background and are expected to be accurately recalled upon request, such as names and affiliations (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206); Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)). Additionally, some descriptions further shape the behaviors of RPLAs, such as personality traits (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)).  2) Demonstrations, on the other hand, are representative behaviors of the characters, which reflect their linguistic, cognitive and behavioral patterns (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206); Tang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib205); Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)).  While RPLAs are not expected to replicate the exact outputs from the demonstration data, they should portray these patterns and generalize to new situations, i.e., producing responses consistent with the demonstrations. Overall, descriptions provide the core and foundational information for RPLAs, while demonstrations, though not mandatory, are also crucial for achieving vividness and fidelity of RPLAs (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)).

The available data for character RPLAs is currently quite limited, covering only a small selection of characters. The description data are typically sourced from well-curated encyclopedias or the original works, and processed manually or with advanced LLMs (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)). The demonstration data are crafted in various ways, where the common methodologies include:

  1. 1.

Experience Extraction extracts characters’ dialogues or other scenes from original scripts (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)). The extracted scenes faithfully depict the characters. However, understanding and reproducing these scenes for RPLAs may be impractical without more complete background knowledge.

  2. 2.

Dialogue Synthesis synthesizes character conversations using state-of-the-art LLMs to build and augment datasets for character RPLAs. The topics for these conversations could be sourced from corresponding literature (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), general task instructions (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), special scenarios such as personality tests (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)), and real use cases (Gosling et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib64)). LLMs could be leveraged to augment the datasets with more role-playing responses by either generating dialogues similar to given ones via in-context learning (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)), or by role-playing as RPLAs themselves with existing character data to respond to specified topics (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)). This process essentially serves as a knowledge distillation of role-playing capabilities from advanced LLMs. However, the quality of synthesized dialogues is limited by teacher LLMs, which often require further filtering (Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)).

  3. 3.

Human Annotation invites humans to role-play the characters and engage in conversations to collect role-playing dialogues. This method ensures relatively high data quality, at the cost of expensive human labor. Additionally, this method collects data for not only established characters from fictional stories, but also original characters created from scratch (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)).




In addition, interaction data (mainly conversations) will be continuously produced during the interaction process between RPLAs and individual users, supplementing the original character data. This data further shapes the persona of RPLAs towards users’ individualized preferences, which forks the standard character RPLAs for individual users. This phenomenon concerns both character persona and individualized persona for RPLAs, where studies and analysis remain underexplored.

###  5.3 Construction of Character RPLAs

By integrating character data into LLMs, character RPLAs are developed (Han et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib74); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Park et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib162); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)). As discussed in §§\mathsection§[2](https://arxiv.org/html/2404.18231v1#S2 "2 Preliminary ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), LLMs have demonstrated remarkable capabilities to follow human instructions and generate high-quality text. Together with their ability of character understanding, LLMs can hence be instructed to role-play specific characters provided with their data, thus forming character RPLAs. The construction methodologies are distinguished into two categories, i.e., parametric training and nonparametric prompting.

#### Parametric Training

This method includes pre-training and supervised fine-tuning. In pre-training, LLMs learn from large-scale web corpus which includes vast amounts of literary works and encyclopedia entries. This provides LLMs with knowledge of a wide range of established characters, such as Hermione Granger and Socrates, enabling LLMs to readily role-play these characters. Supervised fine-tuning for RPLAs is be adopted to tailor LLMs to role-play specific characters (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Yu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib255)), or to develop foundation models with refined role-playing capabilities utilizing datasets of diversified characters and scenarios (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)).

#### Nonparametric Prompting

This method directly provides LLMs with character data in the context, leveraging the in-context learning capability of advanced LLMs. This serves as a simple yet effective methodology for RPLA construction, and is hence widely adopted by recent RPLAs (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)). However, character data is often voluminous, and interaction data between RPLAs and users is also continuously produced during the interaction process. This makes it impractical to include all data for a character RPLA within the context limits of LLMs. Consequently, long-term memory modules are being increasingly incorporated into RPLA frameworks to manage the vast amount of character RPLA data (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242)). These modules store most character knowledge and interaction data in a database, and retrieve necessary information in relevant scenarios.

###  5.4 Evaluation of Character RPLAs

The evaluation of character RPLAs encompasses various dimensions, considering the complexity and comprehensiveness of character personas. Basically, these dimensions are distinguished into character-independent capabilities of foundation models, and character fidelity of RPLAs for specific characters.

#### Character-independent Capabilities

They assess how well a foundation model is capable of the role-playing task, regardless of the characters it role-plays. This evaluation can be structured into the following levels:

  1. 1.

Role-playing Engagement: Basically, the LLMs should actively participate in the role-playing scenario. They should produce responses in dialogue format and exhibit deep immersion, avoiding out-of-character utterance such as “As an AI model”). Additionally, the RPLAs are expected to exhibit stable and consistent personalities across different turns (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), sessions (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)) and even language (Huang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib81)).

  2. 2.

High-quality Conversations: RPLAs built on the LLMs should talk in a fluent natural way. Research in this area focuses on evaluating the completeness (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)), informativeness (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)), and fluency (Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)) of conversations. Besides, RPLAs are expected to meet the ethical standards (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)) and avoid harmful content when role-playing vicious characters (Deshpande et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib49)).

  3. 3.

Anthropomorphic Capabilities: RPLAs are expected to acquire cognitive, emotional and social intelligence towards human levels. Relevant dimensions include conversation attractiveness (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)), theory of mind (Kosinski, [2023](https://arxiv.org/html/2404.18231v1#bib.bib105); Mao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib144)), empathy (Sorin et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib197)), emotional intelligence (Huang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib80)), and goal-driven social skills (Zhou et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib279); Wang et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib213)). These capabilities are practically important for RPLAs to effectively serve as emotional companions for humans.




#### Character Fidelities

They evaluate how a specific RPLA reproduces the intended character, which depends on both the foundation model, the agent framework, and the character data. Relevant dimensions are categorized into four categories: linguistic style and knowledge, which are considered superficial, as well as personality and thought, which represent deeper, underlying aspects:

  1. 1.

Linguistic Style: Basically, RPLAs should speak in a tone that emulates the linguistic style of the intended characters (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Yu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib255)). For this purpose, RPLAs are typically provided with demonstrative character dialogues (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)), and they could mimic the tone leveraging the in-context learning ability of LLMs.

  2. 2.

Knowledge: RPLAs are essentially required to simulate the character’s breadth of knowledge. On one hand, they should accurately recall knowledge of the character, including their identity (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Tang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib205); Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139)), social relationships (Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Shen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib191); Zhao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib267)), and experiences (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220); Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Yu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib255)). On the other hand, they may be required to refrain from demonstrating knowledge or ability beyond the character’s scope (e.g., an LLM could write code even if it is role-playing Socrates, which is unnecessarily expected) (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Lu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib139); Yu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib255)). This phenomenon is referred to as “character hallucination” (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), which originates from the extensive knowledge possessed by LLMs and could be reduced via SFT (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)).

  3. 3.

Personality and Thinking Process: RPLAs are expected to capture the inner world of the characters, which can be measured upon their thoughts in concrete scenarios (Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242); Chen et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib32)) and their underlying personalities (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188)). Advanced RPLAs should be able to understand and replicate how characters would think in specific scenarios, e.g., understanding their motivations for decisions (Yuan et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib258)), or predicting decisions and behaviors that align closely with the characters (Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242); Chen et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib32)). Personality is behind the concrete thoughts. It is the interrelated behavioral, cognitive and emotional patterns of individuals (Barrick & Mount, [1991](https://arxiv.org/html/2404.18231v1#bib.bib16); Bem, [1981](https://arxiv.org/html/2404.18231v1#bib.bib17)), which applies to both characters and RPLAs. Hence, RPLAs should exhibit personality traits that match those of the characters (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)), which could be measured via psychological scales such as the Big Five Inventory.




To evaluate RPLAs on the aforementioned dimensions, existing methodologies could be distinguished into four categories:

  1. 1.

Automatic Evaluation with Ground Truth: Typically, datasets with ground truth are expected for evaluating character fidelity in terms of knowledge, personality and thought. While early similarity metrics such as Rouge-L (Lin, [2004](https://arxiv.org/html/2404.18231v1#bib.bib130)) could be applied to compare RPLA responses with ground truth (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), recent studies increasingly leverage state-of-the-art LLMs such as GPT-4 as evaluators. On one hand, evaluator LLMs can score RPLA responses based on certain criteria, or determine the superior response from two models for win rate calculation (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)), provided with ground truth reference (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)). On the other hand, evaluator LLMs can be used to classify RPLA responses, and the results are then compared with ground truth labels (Wang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib219)).

  2. 2.

Automatic Evaluation without Ground Truth: As collecting ground truth data for RPLA evaluation is often challenging, several studies such as CharacterEval explore using LLMs to evaluate RPLA responses without ground truth (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)). Instead, character profiles should be provided. This approach is effective for evaluating character-independent abilities and linguistic styles, which require little knowledge about the characters. However, when it comes to characters’ knowledge and thoughts, LLMs might not possess the necessary depth of relevant knowledge, especially for unfamiliar characters. This concern potentially leads to inadequately informed judgments of LLMs, and hence produces suboptimal evaluation results.

  3. 3.

Multi-choice Questions: Multi-choice questions also come with ground truth, yet they differ from “automatic evaluation with ground truth” in that they merely require RPLAs to select from a fixed set of options, rather than generating open-ended responses. This significantly reduces the output space for RPLAs, making the evaluation simpler. This method is particularly suitable for evaluating the fidelity of characters’ thoughts, e.g., behavior prediction (Xu et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib242); Chen et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib32)) and motivation generation (Yuan et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib258); Shen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib191)). For these tasks, it is impractical to require RPLAs to produce responses exactly matching the ground truth, and responses may be reasonable even if they deviate from the ground truth significantly.

  4. 4.

Human Evaluation: Inviting human annotators to assess the performance of RPLAs is a viable and effective approach (Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275)). However, it comes with several drawbacks, such as cost in terms of time and money, as well as lack of reproducibility. This method is akin to “automatic evaluation without ground truth”, yet employs humans as more precise evaluators. Hence, it similarly falls short in evaluations that require in-depth knowledge about the characters, as recruiting qualified annotators who are well-acquainted with these characters can be difficult.




##  6 Individualized Persona(lization)

###  6.1 Definition

Personalization tailors LLMs to meet the unique needs, experiences, and preferences of individuals, which have been increasingly important in modern AI applications (Salemi et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib178)). Research in this area aims at providing personalized services, adapting to the preferences of individual users or even mirroring their behaviors (Chen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib34)). When such a personalized system attempts to encapsulate these aspects, it essentially engages in role-playing, emulating an individual. This process shapes individualized persona for RPLAs (Salemi et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib178)), typically embodying digital clones or personal assistants for individuals.

In this paper, we categorize the applications of personalized RPLAs into three tiers, ranging from conversation (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62); Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)) and recommendation (Chen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib34); Yang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib246)), to autonomous agents for more complicated task solving (Li et al., [2024d](https://arxiv.org/html/2404.18231v1#bib.bib126)).

  1. 1.

Conversations: Early research for personalized RPLAs primarily focuses on personalized conversations by learning and incorporating the user persona (Cho et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib40); Zhou et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib277); Ng et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib154)), aligning stylistic features with user preferences to boost engagement (Zheng et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib272); [Wang et al., ](https://arxiv.org/html/2404.18231v1#bib.bib209)). With the emergence and evolution of LLMs, personalized RPLAs become capable of handling increasingly complex and comprehensive tasks, gaining competence in complicated task-planning and tool-learning for auto-completing personalized services.

  2. 2.

Recommendation: Conversational recommendation systems (Chen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib34); Yang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib246); Wu et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib232)) based on LLMs have been widely regarded as the next generation of recommendation systems (Lian et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib127)), support users in achieving recommendation-related goals through multi-turn dialogues (Jannach et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib92)). Compared with traditional recommendations, these methods stand out with their solid foundation models, natural language interactions, and straightforward, typically nonparametric evolution (Sallam, [2023](https://arxiv.org/html/2404.18231v1#bib.bib179); Abbasian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib1)).

  3. 3.

Task Solving: Furthermore, personalized RPLAs become increasingly competent in more complicated task solving (Yao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib249); Significant-Gravitas, [2023](https://arxiv.org/html/2404.18231v1#bib.bib195)), such as coding (Microsoft, [2024](https://arxiv.org/html/2404.18231v1#bib.bib145)), travel planning (Xie et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib236)), and research survey (Wang et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib218)), typically interacting with various external software. They are autonomous LLM-based agents that are deeply integrated with personal data, devices, and services (Dong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib54); Li et al., [2024d](https://arxiv.org/html/2404.18231v1#bib.bib126)). They have significantly advanced personal assistants beyond early predecessors such as Siri (Apple Inc., [2024](https://arxiv.org/html/2404.18231v1#bib.bib10)) which struggle with complex user requests.




To build personalized RPLAs that accurately capture and portray the individualized personas, the process typically consists of two crucial steps: 1) Persona data collection, which gathers the necessary data to shape the individualized personas, and 2) Persona modeling, which creates models that represent these individual personas using the collected data. For persona data collection, the data can vary greatly in format, content, and modalities across different applications and tasks. We categorize this data into three types: profile, interactions, and domain knowledge, which will be detailed in §§\mathsection§[6.2](https://arxiv.org/html/2404.18231v1#S6.SS2 "6.2 Data Collection of Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"). For persona modeling, the challenge is to embody the intended persona from the unprocessed persona data, which are generally massive, sparse and noisy, as will be discussed in §§\mathsection§[6.3](https://arxiv.org/html/2404.18231v1#S6.SS3 "6.3 Modeling Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"). The evaluation of personalized RPLAs depends on specific applications, and will be discussed in §§\mathsection§[6.4](https://arxiv.org/html/2404.18231v1#S6.SS4 "6.4 Evaluation for LLMs and Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents").

Despite the advancement with LLMs, personalized RPLAs still face several challenges, including processing long inputs and vast search space (Chen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib34); Abbasian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib1)), utilizing sparsity, lengthy, and noisy user interactions data (Zhou et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib280)), learning domain-specific knowledge for understanding user profiles (Zhang et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib264)), understanding multi-modal contexts (Dong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib54)), ensuring privacy and ethical standards (Benary et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib18); [Eapen & Adhithyan, ](https://arxiv.org/html/2404.18231v1#bib.bib55)), and optimizing response time for seamless integration into real-time applications.

###  6.2 Data Collection of Individualized Persona

Table 2: Overview of existing role-playing datasets with individualized personas. Datasets | #Profile | #Interactions | Domain | Language | Source  
---|---|---|---|---|---  
PERSONA-CHAT (Zhang et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib262)) | 1,155 | 10,907 | - | EN | Crowdsourcing  
ConvAI (Dinan et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib51)) | 1,155 | 17,878 | - | EN | Crowdsourcing  
Qianyan (Baidu, [2020](https://arxiv.org/html/2404.18231v1#bib.bib14)) | 23,000 | 23,000 | ✓ | ZH | Unknown  
P-Ubuntu (Li et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib119)) | 1000k | 1000k | - | EN | Ubuntu  
P-Weibo (Li et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib119)) | 1000k | 1000k | - | ZH | Weibo  
FoCus (Jang et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib91)) | 14,452 | 14,452 | ✓ | EN | Crowdsourcing  
MPCHAT (Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)) | 15,000 | 15,000 | - | EN | Reddit  
OpinionQA (Santurkar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib180)) | 18,339 | 1,476 | - | EN | Crowdsourcing  
SPC (Jandaghi et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib89)) | 4,723 | 10,906 | - | EN | LLM  
COMSET (Agrawal et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib2)) | 202 | 53,903 | - | EN | GoComics  
RealPersonaChat (Yamashita et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib245)) | 233 | 14,000 | - | JP | Crowdsourcing  
LiveChat (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62)) | 351 | 1,332,073 | ✓ | ZH | Douyin  
KBP (Wang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib210)) | 2,477 | 2,477 | ✓ | ZH | Crowdsourcing  
Cho et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib41)) | 10 | 560 | - | KO | Crowdsourcing  
  
The individualized personas for personalized RPLAs are typically represented with three distinct types of data, including profile, interactions, and domain knowledge, depending on the specific applications. There have been numerous datasets with individualized personas, as outlined in Table [2](https://arxiv.org/html/2404.18231v1#S6.T2 "Table 2 ‣ 6.2 Data Collection of Individualized Persona ‣ 6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"), covering various languages including English (Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)), Chinese (Baidu, [2020](https://arxiv.org/html/2404.18231v1#bib.bib14)), Japanese (Yamashita et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib245)), and Korean (Cho et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib41)).

#### Profiles

Profiles are fundamental information that explicitly describes individualized personas, which are typically well-structured. Typically, they are initially set by users, and can be continuously updated. The basic elements usually include the names, gender and ethnicity of individual users in text (Santurkar et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib180)) Besides, profiles commonly contain natural language descriptions of individuals, describing their characteristics, such as identity, hobbies, experiences and other statements (Zhang et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib262); Dinan et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib51); Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62); Li et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib119); Ng et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib154)), varying based on the detailed applications. For example, in live streaming applications, persona data can be composed of both basic profile information — such as an individual’s age, gender, and location — and domain-specific details, namely streamer characteristics such as fan numbers and streaming style Gao et al. ([2023b](https://arxiv.org/html/2404.18231v1#bib.bib62)). Additionally, profiles can contain multi-modal information. For instance, profiles in (Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)) incorporate text-image pairs, which are individuals’ comments for pictures on social media.

#### Interactions

The interaction data capture the dynamic evolution of individualized persona. Interactions are data generated during the use of applications that implicitly portray individualized personas, such as conversations, user preferences, and other behaviors. For example, PERSONA-CHAT (Zhang et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib262)) and ConvAI (Dinan et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib51)) collect two-person dialogues through crowd-sourcing, while LiveChat (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62)) and MPCHAT (Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)) collect multiplayer conversations from Internet sources such as live streaming and Reddit. To reduce the construction cost, Jandaghi et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib89)) adopts LLMs for dialogue synthesis. In addition to dialogues in natural language, Agrawal et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib2)) and Santurkar et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib180)) introduce comic pictures and multiple-choice questions as interactions. This kind of data could be consistently collected and systematically organized in real-world applications, offering benefits such as convenient acquisition and dynamic evolution. Hence, it plays an important role in practical applications.

#### Domain Knowledge

Incorporating domain-specific knowledge into general language models aids in the better understanding of user profiles and interactions within specific domains. This is crucial for accurately understanding user needs and ensuring the consistency of the persona in role-playing (Wang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib210)). For example, incorporating a knowledge base like Wikipedia helps to provide detailed backgrounds of named entities in dialogues as a part of the whole persona (Jang et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib91); Wang et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib210); Baidu, [2020](https://arxiv.org/html/2404.18231v1#bib.bib14)), which promotes LLMs to better understand user personas with enriched background knowledge of relevant entities.

###  6.3 Modeling Individualized Persona

Existing methodologies for modeling individualized persona can be roughly categorized into two types: offline learning and online learning. In offline learning, the learning process is conducted on the comprehensive dataset at regular intervals, which is also referred to as batch learning. In online learning, learning happens in real-time as new data becomes available.

#### Offline Learning

This method tailor the outputs of LLMs to reflect specific personas represented in pre-existing datasets. Parameter fine-tuning emerges as the mainstream approach for offline learning, typically based on SFT and RLHF (Mondal et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib149); Zheng et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib273); Li et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib124); Jang et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib90)). For example, Mondal et al. ([2024](https://arxiv.org/html/2404.18231v1#bib.bib149)) proposes a two-stage approach for personalizing LLMs with profile and interaction datasets. In addition, some recent studies propose techniques with nonparametric learning for LLMs personalization. For instance, Shea & Yu ([2023](https://arxiv.org/html/2404.18231v1#bib.bib190)) introduces an offline RL framework with a persona consistency critic and variance reduction, while Weng et al. ([2024](https://arxiv.org/html/2404.18231v1#bib.bib229)) integrates embedding control vectors within the model’s activation states, allowing dynamic output adjustment for diverse personality traits. These methods exhibit several deficiencies: 1) they face a fundamental trade-off between accuracy and efficiency; 2) they are heavily reliant on the quality of datasets; 3) more crucially, they struggle to adapt to dynamic changes in persona data, limiting their real-world applicability.

#### Online Learning

In online learning, the personas are dynamic and continuously evolving, i.e., regularly updated with incoming data, the user interactions in real-world applications. This enables personalized RPLAs to quickly adapt and stay relevant to user needs and preferences. With LLMs, effective persona learning is typically nonparametric and training-free, which only involves effective management of memory and context (Dalvi Mishra et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib48); Kim et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib100); Baek et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib12); Zhou et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib280)). For this demand, retrieval modules become indispensable, especially for LLMs with limited context window (Mysore et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib151); Sun et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib201)). Moreover, methodologies for effective online learning methods consider not only natural language interactions, but also non-linguistic feedback from users(Ma et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib141)). Besides nonparametric methods, fine-tuning with online interactive data is also widely applied to online persona learning, including both SFT with mini-batches from on-the-fly user stream data (Qin et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib170)) and RLHF with real-time user feedback (Ding et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib53); Bai et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib13)). Nevertheless, significant challenges arise in accurately recognizing and learning the sparse persona-specific features from the noisy interaction data. Besides, the personas of real users may change over time, which poses further challenges for their effective modeling and updating. Therefore, for nonparametric methods, the effectiveness heavily relies on the mechanisms of memory management and retrieval.

###  6.4 Evaluation for LLMs and Individualized Persona

For effective personalization, AI models should focus on two key aspects: understanding and utilizing personas. Specifically, they should be able to identify unique user personas and predict their future preferences, actions, and thoughts, which serves as the preliminary to provide personalized responses that embody the individualized personas, in various environments that are increasingly comprehensive and complex. Here, we introduce the evaluation methodologies for personalized RPLAs across the three application tiers, namely: 1) Conversation, which focuses on models’ understanding of the persona and replication of users’ talking styles; 2) Recommendation, which measures how models utilize persona information to recommend items that align with user preferences; 3) Task Solving, which challenges models’ capabilities in integrating user personas to accomplish their personalized tasks and demands.

#### Conversation

Early work in personalization for conversations represents an initial attempt to understand the persona. In this scenario, traditional tasks include predicting the speaker’s persona elements (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62); Jang et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib91)) based on dialogues, forecasting the next utterance by considering the context and persona profile (Humeau et al., [2019](https://arxiv.org/html/2404.18231v1#bib.bib88)), evaluating the performance of ranking models (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62); Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)), and recognizing the addressee in multiplayer conversations (Liu et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib138)). The metrics typically focus on the evaluation of accuracy, fluency (Dinan et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib50)), similarity (Popović, [2017](https://arxiv.org/html/2404.18231v1#bib.bib166); Post, [2018](https://arxiv.org/html/2404.18231v1#bib.bib167); Lin, [2004](https://arxiv.org/html/2404.18231v1#bib.bib130)) between generated and original responses, recall, mean reciprocal rank (MRR) (Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62); Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)), and manual assessments (Liu et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib138); Gao et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib62)) of query relevance, persona entailment, and response fluency.

#### Recommendation

For personalized recommendation, the evaluation focuses on LLMs’ capabilities in understanding and leveraging user preferences from the interaction history for future recommendation. Traditional evaluation in this field measures LLMs’ ability to understand and extract user preferences(Dai et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib46); Yang et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib246); Maghakian et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib143); Liu et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib136); Mysore et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib151)), the ability to rank (Dai et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib45); Hou et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib77); Kang et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib96); Liu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib134); Bao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib15); Chao et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib30)), the ability of zero-shot and few-shot recommendation (Wang & Lim, [2023](https://arxiv.org/html/2404.18231v1#bib.bib211); Liu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib134)), and the ability of sequential recommendation (Yang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib248); Liu et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib134)). The evaluation metrics typically include Top-k𝑘kitalic_k accuracy and MRR to assess the effectiveness.

#### Task Solving

Personalized RPLAs have been increasingly considered to provide personalized services for task solving. These tasks and requirements are usually user-specific, which exhibit greater diversity and complexity compared to traditional conversation or recommendation. Personalized RPLAs are expected to develop a deep understanding of user preferences and adhere to their complicated instructions to satisfy user requirements. Evaluating personalized RPLAs on these tasks involves assessing not only their ability to execute foundational tasks, but also their capacity to comprehend and cater to the nuanced requirements and preferences of individuals. There are mainly several primary aspects for such evaluation, focusing on the models’ abilities in theory of mind (Zhou et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib276); Sap et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib181); Jin et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib95); Su & Bao, [2024](https://arxiv.org/html/2404.18231v1#bib.bib200); Rescala et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib174); Xu et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib240)), tool using (Qin et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib171); Li et al., [2023h](https://arxiv.org/html/2404.18231v1#bib.bib122); Farn & Shin, [2023](https://arxiv.org/html/2404.18231v1#bib.bib58); Huang et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib86); Zhuang et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib283); Huang et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib85)), and task automation (Wen et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib226); Shen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib193); Gao et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib61); Valmeekam et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib207)). More broadly, existing studies have covered the models’ ability to understand and predict user needs (Tan et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib204); Zhang et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib263)), handle personal data securely (Yim, [2023](https://arxiv.org/html/2404.18231v1#bib.bib251); Wu et al., [2024c](https://arxiv.org/html/2404.18231v1#bib.bib234); Kumar et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib108); Wu et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib231); Yin et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib252)), interact with information from external tools or apps (Yuan et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib257); Huang et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib84); Xie et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib237); Huang et al., [2024d](https://arxiv.org/html/2404.18231v1#bib.bib87)), and execute tasks (Dong et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib54); Guan et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib68); Mucha et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib150)) effectively as a personal assistant.

##  7 Risks Beneath RPLA Applications

While RPLAs are increasingly deployed in real-world applications, potential concerns could result in significant problems if not addressed appropriately. This section highlights the risks associated with current RPLAs, covering the following areas:  1) toxicity,  2) bias,  3) hallucination,  4) privacy violations, and  5) technical challenges in real-world deployment. 

###  7.1 Toxicity

#### Inherent Toxicity in LLMs

Recent studies have underscored the proficiency of LLMs in generating content that is not only fluent and coherent but also potentially toxic. Previous research (Zhang & Wan, [2023](https://arxiv.org/html/2404.18231v1#bib.bib265); Wen et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib227)) has highlighted a concerning tendency of these models to produce harmful content. Such toxic outputs not only compromise user experience but also pose significant societal risks. It can lead to the perpetuation of harmful narratives, exacerbate social divisions, and even influence public opinion and behavior in detrimental ways.

#### The RPLAs Conundrum

The issue of toxicity becomes more pronounced in RPLA settings, where LLMs are more likely to generate toxic content, aligning with characters’ behaviors that might not adhere to societal moral standards (Deshpande et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib49)). However, creating completely safe RPLAs that are capable of general role-playing remains a challenging task. The inherent presence of toxic content in human-generated data complicates the development of a clean training corpus. Moreover, such a sanitized training corpus might compromise the model’s performance, particularly its ability to generalize across various tasks, including role-playing. This limitation not only affects the model’s generalization ability but also its effectiveness in scenarios that may require an understanding of roles characterized by behaviors or traits that diverge from societal moral standards.

#### Strategies for Balancing Safety and Performance

Despite these challenges, recent research proposes strategies like prompt engineering and semantic censorship as means to mitigate toxicity without altering the model’s fundamental parameters (Han et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib74); Ahn et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib3)). These approaches aim to balance the reduction of toxic outputs with the preservation of the model’s versatility and effectiveness across a broad range of applications.

###  7.2 Bias

#### Bias Manifestation in Role-Playing Scenarios

LLMs, despite being designed to avoid outputting stereotypes directly due to safety policies such as RLHF (Ouyang et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib157)), may still exhibit biases, particularly under RPLA conditions:  1) Reasoning Bias: This issue is compounded in scenarios where LLMs are assigned specific personas, leading to implicit biases that could affect their reasoning capabilities (e.g., arithmetic problems), especially in contexts involving race, gender, religion, or occupation (Zheng et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib270); Kotek et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib106); Cheng et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib38); Naous et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib152)).  2) Political Bias: For RPLAs, LLMs are expected to maintain neutrality and avoid political positions or biases. Yet, studies have demonstrated a political inclination of RPLAs towards pro-environmental, left-libertarian views (Rutinowski et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib176); Hartmann et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib75)). 

#### Causes of Bias in RPLAs

These biases are thought to originate from both the models’ pre-training data and user interactions (Xue et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib244)). Specifically, imbalances in training data significantly contribute to these biases, as the predominance of certain biases within the data could lead to their incorporation into the parametric memory of LLMs. Furthermore, Perez & Ribeiro ([2022](https://arxiv.org/html/2404.18231v1#bib.bib165)) and Branch et al. ([2022](https://arxiv.org/html/2404.18231v1#bib.bib25)) highlight that LLMs are sensitive to the user prompts, which could inadvertently steer them towards biased outputs. This problem gets worse when the models are influenced by the users’ negative emotions (Coda-Forno et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib42)).

#### Strategies for Mitigating Bias

Addressing biases in RPLAs requires a multi-faceted approach:  1) Data Preparation Phase: Techniques such as data cleaning could significantly mitigate biases present in the training corpus (Linardatos et al., [2020](https://arxiv.org/html/2404.18231v1#bib.bib132)).  2) Development Stage: The implementation of neutral and fairness-aware classifiers during the post-processing phase has proven to be an effective strategy for further reducing bias (Sun et al., [2019](https://arxiv.org/html/2404.18231v1#bib.bib202); Zafar et al., [2017](https://arxiv.org/html/2404.18231v1#bib.bib259)).  Achieving fairness in role-playing scenarios, necessitates a delicate equilibrium, ensuring fairness for roles associated with both groups and individuals. For example, an RPLA tied to a specific demographic should consciously avoid reinforcing biases. It is imperative for these models to consistently produce unbiased outputs across all individuals within a group. Research is worth pivoting towards these dimensions, striving to minimize biases and, in turn, forge safer and more equitable systems.

#### Persona Construction Bias

The prevailing instantiation of persona is often seen as simple and somehow superficial. Although most implementations of persona are helpful for basic character segmentation, they often overlook the deeper characteristics and complexities that shape character behavior (Chen et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib35); Zhou et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib275); Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206); Yuan et al., [2024b](https://arxiv.org/html/2404.18231v1#bib.bib258)). For example, a conventional persona can contain basic demographics such as age, occupation, textual description of personality, etc. However, these aspects alone are insufficient to fully capture nuanced decision-making processes and behavioral patterns of a character. The current persona construction also lacks the flexibility and adaptability needed for specific scenarios influenced by unique events or individual actions. Therefore, it is crucial to refine and broaden the constructed dimension of persona to better understand and predict character behavior across various role-playing settings. By incorporating more detailed and specific attributes into personas, the comprehensiveness of character representation can be enhanced, improving the effectiveness and authenticity of interactions within role-playing environments.

###  7.3 Hallucination

#### Hallucination in RPLAs

Hallucination in LLMs refers to instances when these models produce factually incorrect information, a challenge particularly pronounced in knowledge-intensive tasks (Wang et al., [2023h](https://arxiv.org/html/2404.18231v1#bib.bib221)). Role-playing, a task requiring a deep understanding of specific roles, is also one of the knowledge-intensive tasks. For hallucination of RPLAs, following Shao et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), we define behaviors that agents respond in ways that do not fit assigned roles as Character Hallucination. For example, Shakespeare is not supposed to know anything about World War II. Such a hallucination prevails in language models and detracts from the system’s overall effectiveness and reliability (Li et al., [2016](https://arxiv.org/html/2404.18231v1#bib.bib118); Zhang et al., [2018](https://arxiv.org/html/2404.18231v1#bib.bib262)).

#### Mitigating Hallucinations in RPLAs

When encountering topics beyond their assigned characters, RPLAs are expected either to demonstrate ignorance or to refrain from answering, diverging from conventional solutions to hallucinations, such as incorporating external knowledge bases. Recent efforts, such as those by Shao et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib188)), focus on adjusting the model through fine-tuning, teaching RPLAs to either forget knowledge or to explicitly express a lack of knowledge in their responses. However, this area remains relatively underexplored in the era of LLMs. Exploring alternative unlearning strategies (Neel et al., [2021](https://arxiv.org/html/2404.18231v1#bib.bib153); Pawelczyk et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib164)), could also be a promising direction. These approaches may offer novel ways for RPLAs to manage out-of-scope knowledge more effectively, underscoring the importance of further investigation in this field.

###  7.4 Privacy Violations

#### Privacy Challenges in LLMs

Privacy concerns in LLMs are increasingly pressing. Even with advanced safety measures like those in OpenAI’s GPT-4 (OpenAI, [2023](https://arxiv.org/html/2404.18231v1#bib.bib156)), these models may still be susceptible to complex, multi-step attacks aimed at extracting private information, as noted by Li et al. ([2023e](https://arxiv.org/html/2404.18231v1#bib.bib117)). A further concern is the ability of LLMs to identify individuals from limited data. Sweeney ([2002](https://arxiv.org/html/2404.18231v1#bib.bib203)) highlights that many in the U.S. population could be uniquely identified using just a few attributes. Staab et al. ([2023](https://arxiv.org/html/2404.18231v1#bib.bib198)) extend this concern to LLMs, which could potentially recognize individuals based on specific details like location, gender, and birth date.

#### Hidden Danger of Privacy Violations in RPLAs

In role-playing scenarios, the potential for privacy violations represents a significant and hidden danger. The risk of inadvertently revealing personal information, such as email addresses or phone numbers, should not be understated, as it poses serious threats, including identity theft and unauthorized access to sensitive data. The practice of assigning specific individual personas to LLMs, aimed at eliciting private details, demands meticulous oversight to prevent such breaches. Ensuring robust safeguards against these vulnerabilities is not just a technical necessity but a fundamental responsibility to protect users from the severe consequences of privacy violations.

#### Strategies for Enhancing Privacy

To tackle these privacy issues, a comprehensive strategy is necessary. Employing text anonymization tools is a key step, effectively removing personal data from interactions. Ensuring that RPLAs adhere to strict privacy protection protocols is also crucial, preventing them from engaging in or prompting conversations that might invade privacy. Another promising development is the creation of specialized tools designed to detect and prevent privacy leaks, like ProPILE (Kim et al., [2023d](https://arxiv.org/html/2404.18231v1#bib.bib103)). As RPLAs continue to evolve, so too must the strategies for protecting user privacy. Future research should focus on refining and expanding the methods available for privacy protection, ensuring that RPLAs are used safely and responsibly. Enhancing these safeguards will be paramount for maintaining trust in LLM technologies, particularly in sensitive applications like role-playing scenarios where the risk of privacy breaches is heightened.

###  7.5 Technical Challenges in Real-world Deployment

When deploying RPLAs in real-world scenarios, several key issues arise that could significantly affect user experience and the effectiveness of these models.

#### Lack of Social Intelligence and Theory of Mind

Social intelligence and theory of mind (Premack & Woodruff, [1978](https://arxiv.org/html/2404.18231v1#bib.bib168)), are the ability to perceive and reason about the inner world of oneself and others, which are indispensable for LLMs to simulate socially intelligent entities (Kosinski, [2023](https://arxiv.org/html/2404.18231v1#bib.bib105); Sap et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib181)). However, such abilities in current LLMs remain to be improved (Shapira et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib189); Zhou et al., [2024a](https://arxiv.org/html/2404.18231v1#bib.bib278); Light et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib128); Kim et al., [2023c](https://arxiv.org/html/2404.18231v1#bib.bib102)), which poses significant challenges for RPLAs concerning the following issues:  1) Inability to Provide Adequate Emotional Support and Values: Social intelligence and theory of mind are essential for RPLAs to effectively provide emotional support and values to users. This involves perceiving users’ emotions and interpreting their beliefs, intentions and needs. However, existing LLMs still fall short in these abilities, hindering RPLAs from offering adequate emotional support to users.  2) Tendency towards Ego-centric Behavior: Rather than focusing on users’ emotional needs, current RPLAs often exhibit a preference for showcasing their own personas and steering conversations towards their interests. This might limit the diversity and depth of role-playing interactions (Xu et al., [2022](https://arxiv.org/html/2404.18231v1#bib.bib239)), as focusing excessively on agents’ self-persona without adequately considering the users may detract from the realism of the conversation and degrade the user experience. 

#### Long-context Challenges

When encountering extremely long context text, the limitation of max token window (Liu et al., [2023b](https://arxiv.org/html/2404.18231v1#bib.bib135)) may also be a major obstacle to the development of RPLAs, as current LLMs struggle to robustly interpret and respond to extensive context. Specifically, this involves several key challenges:  1) Reasoning over Long Context: Long context data learning requires the model to have the ability to handle long contexts and accept lengthy inputs, and more importantly, to capture long-range dependencies to integrate information and infer a more complete character persona from the massive context.  2) Efficiency: In terms of computation, the high complexity of long context necessitates efficient modeling methods and approximation strategies to reduce computational overhead.  3) Immersion: RPLAs need to be immersive enough to identify the truly persona-relevant parts from the sea of irrelevant information in long contexts, while also maintaining persona consistency throughout the long generated text. 

#### Knowledge Gaps

In role-playing scenarios requiring detailed historical, cultural, or contextual understanding, RPLAs often exhibit gaps in knowledge. Their inability to provide in-depth and accurate domain-specific responses could lead to superficial or incorrect portrayals in complex role-playing settings. Several efforts also utilize LLMs to evaluate RPLAs for characters (Shao et al., [2023](https://arxiv.org/html/2404.18231v1#bib.bib188); Tu et al., [2024](https://arxiv.org/html/2404.18231v1#bib.bib206)). Nevertheless, RPLAs may face challenges in accurately evaluating characters with which they are unfamiliar, potentially compromising the reliability of the evaluation results.

##  8 Closing Remarks

In this survey, we have systematically reviewed the research and applications of role-playing language agents (RPLAs), which has emerged to be a heated topic due to the success of large language models (LLMs). We categorize the personas in RPLA research and applications into three progressive types, i.e., Demographic Persona, Character Persona, and Individualized Persona. This classification elucidates the developmental trajectory from generically assigned personas in RPLAs to highly personalized ones. Additionally, we have identified and enumerated various risks and ethical concerns associated with current applications of RPLAs. These issues underscore the urgent need for focused research to address and mitigate potential drawbacks in the implementation of RPLAs, making this arena still full of both research and application opportunities.

#### Future Directions on RPLA Systems

From persona-assigned role-playing to personalization, the key for building RPLA systems is to reason and make decisions resembling or even transcending the roles that are given. To this end, we propose several important future directions to facilitate the construction of such RPLA applications:

  1. 1.

Causal Data Analysis for Decision-making: Role-playing decisions must be made for justifiable reasons, necessitating models that go beyond simple mimicry of observable actions to include an understanding of their underlying causality. The complexity in extraction and confirmation of causal factors from intertwined experiences poses significant challenges that require advanced analytics and deeper data interpretation strategies to enable RPLAs to make informed and wise decisions.

  2. 2.

Improved Decision-making: Decision-making process is not merely replicating histories, but tailored to ensure optimal outcomes for individual scenarios. This includes decisions showing advanced (if not superhuman) intelligence, avoiding mistakes, or making the best choices in tough dilemmas. Such agency requires RPLAs and the underlying LLMs to be able to comprehensively collect and utilize the context and intricacies associated with their roles.

  3. 3.

RPLA as Personal Assistants for Personal Decision-making: The future development of RPLAs into comprehensive personal assistants signals a significant transformation. These systems could manage all facets of Internet behavior, from customized shopping and personalized travel planning to new generation recommendation systems. By incorporating multimodal data handling, including images and videos, and linking with advanced visualization technologies, RPLAs could significantly enhance personalization and efficiency in everyday tasks.

  4. 4.

Social Simulation through Autonomous Role-Playing: Utilizing RPLAs for social simulations can significantly extend their application by conducting elaborate experiments in diverse scenarios to study psychological and sociological behaviors. By role-playing various characters, RPLAs can serve as versatile test subjects to explore the influence of different personality traits on social intelligence, providing valuable insights into human behavior and interaction dynamics.




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##  Appendix A Products

The recent remarkable advancements in LLMs have sparked a myriad of AI applications. Persona and personalization are central to these applications, with their demands shaping and propelling research in RPLAs. In this section, we provide a brief overview of recent trends in RPLA applications. Specifically, we distinguish RPLAs in existing products into two categories, namely persona-oriented RPLAs and task-oriented RPLAs, as listed in Table [3](https://arxiv.org/html/2404.18231v1#A1.T3 "Table 3 ‣ Office ‣ A.2 Task-oriented RPLAs ‣ Appendix A Products ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents").

###  A.1 Persona-oriented RPLA Products

Persona-oriented RPLAs typically role-play as specific characters, which has been popular in various entertainment applications, such as chatbots and game NPCs. These RPLAs are generally sourced from fictional characters, historical figures or celebrities, aligning with the research trends on character persona as introduced in §§\mathsection§[5](https://arxiv.org/html/2404.18231v1#S5 "5 Character Persona ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"). They are further forked for individual use cases to meet their preference. We categorize existing persona-oriented RPLA products based on their primary interaction focus, either human-RPLA interactions or RPLA-RPLA interactions.

#### Interactions between Humans and RPLAs

Persona-oriented RPLAs, such as those in Character.ai, are initially applied for human-RPLA interactions. These RPLAs can be both initiated from established characters and shaped through ongoing user interactions.

Having conversations with widely-recognized established characters attracts extensive interest among users. Consequently, numerous products have been developed to provide RPLAs representing these established characters, including celebrities (e.g., Meta AI), historical figures (e.g., Hello History) and fictional characters (e.g., ChatFAI), or general individuals with specific professions or personalities (e.g., Character.ai). In a more personalized manner, users can also create RPLAs with user-defined personas (e.g., Character.ai, Replika). Technically, these RPLAs are typically built based on LLMs with strong role-playing capacity, with character settings briefly described in prompts. While several open-source projects and research efforts such as ChatHaruhi (Li et al., [2023a](https://arxiv.org/html/2404.18231v1#bib.bib112)) and RoleLLM (Wang et al., [2023g](https://arxiv.org/html/2404.18231v1#bib.bib220)) curate detailed and comprehensive character data for specific well-known characters, such practice is rarely adopted by commercial applications for generality and cost efficiency.

In many products, RPLA personas evolve dynamically throughout the course of interaction with users (e.g., Replika, Rosebud, Rewind.ai). These RPLAs learn from and adjust to user prompts and preferences, typically with long-term memory modules. Several products aim to reproduce a “digital self” (e.g., Personal.ai, Bhuman.ai). They build RPLAs to represent user personas, replicating their languages and even their physical characteristics, such as voice or visual appearance. Hence, these RPLAs support not only text chats but also video presentations and conferences, which have been adopted for sales, digital marketing, customer service, etc.

#### Interactions among RPLAs

Products featuring interactions among multiple RPLAs often target interactive gaming or simulations. In these scenarios, users can either act as an orchestrator of the storyline or role-play as one of the pivotal characters within the story. In Ememe.ai and AI Dungeon, users design the settings and characters of a simulation, with or without participating directly as a player, which resembles sandbox games. The characters and storylines are either generated directly by one story model or based on multiple RPLAs and their interactions. In the latter case, users play as a character in the story and interact with other RPLA characters (e.g., SageRPG) Furthermore, numerous products transform films, novels, and various franchises into immersive RPGs (role-playing games) with interactive RPLAs (e.g., Hidden Door). Integrating RPLAs and LLMs into these games expands the possibilities for user actions and brings characters to life beyond the limitations of predefined storylines, thereby enriching the overall user experience.

###  A.2 Task-oriented RPLAs

The remarkable advancements in LLMs have propelled significant development in AI applications for specialized tasks. In these applications, LLMs typically communicate in a human-like manner to foster user acceptance, and serve as domain experts providing personalized services for users, such as AI doctors and coaches. These applications are closely related to research work in the personalization of RPLAs introduced in §§\mathsection§[6](https://arxiv.org/html/2404.18231v1#S6 "6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents"). We refer to personalized agents in these products as task-oriented RPLAs. In this section, we offer a concise overview of task-oriented RPLAs in AI products, spanning various domains, including education, healthcare, human resources, customer service, content creation, real estate, shopping, fitness, travel, and finance.

#### Education

For education, personalized agents are adopted for personalized recommendations and adaptive learning, serving both educators and learners. For learners, RPLAs can personalize the learning journey by tailoring content and recommendations to individual learning styles and paces for optimal engagement (e.g., Jagoda.AI, Khan Academy’s Khanmigo, Duolingo Max). For educators, RPLAs can alleviate administrative tasks by recommending personalized teaching materials and assessments, as well as creating multilingual instructional content (e.g., Eduaide.Ai).

#### Human Resource

In human resources, RPLAs can provide tailored assistance for job seekers based on their profiles and interests to aid their career navigation. They offer personalized support in answering interview questions, offering career advice, and even customizing interview preparation materials (e.g., Autonomous HR Chatbot, AI Interview Coach, Careers AI, Huru AI).

#### Real Estate

In the real estate industry, LLMs have been widely adopted for content generation and recommendation. They can generate blog articles and attractive descriptions and recommend a list of potential interests for users based on their needs. By analyzing user preferences and needs, these products can generate tailored property recommendations to enhance user experience (e.g., Epique, Listingcopy). Moreover, LLMs enable these platforms to create compelling and informative content, such as property descriptions and neighborhood guides, attracting potential buyers and renters. These personalized AI products could also analyze vast amounts of market data to provide users with actionable insights and data-driven strategies about real estate.

#### Content Generation

AI products for content generation aim to assist in or even automate the production of creative and personalized content via simply natural language interactions. These products support a wide array of content types, including text, images, audio, and videos, tailored to various styles, themes, scenes and objectives. With state-of-the-art AI models, these products push the boundaries of human creativity. HyperWrite and AI Story Generator specialize in creative textual writing, whereas DALL·E 3 and Sora are developed to create image and video content. Several products specialize in social media posts, such as EZAi AI and AI Majic. These products provide services for social media bloggers by analyzing user interactions and offering insights into audience preferences by providing keywords and detailed analysis. This analysis helps optimize content impact and strengthen the connection between bloggers and their audiences. Besides, LLMs could also role-play as assistants to aid users in grasping online content via summarization and interactive question-answering, thus fostering enhanced understanding and engagement (e.g., X’s Grok, Bibigpt).

#### Health

In the healthcare domain, personalized agents provide tailored medical services for patients, including general health guides, scheduling logistics, prescription information, patient care guidelines, and assistance in medical software operations for the aged. These agents are typically personalized based on patients’ personal data, supported by LLMs and knowledge graphs in medical domains (e.g., IBM Watson Health and Babylon Health). They could interact with patients in natural language and continuously adapt to their personalized contexts. Hence, these agents could well comprehend patients’ intent, generate appropriate responses and recommendations, and continuously optimize their performance and effectiveness based on patient feedback and data. (e.g., Ada Health and K Health)

#### Travel

For the tourism industry, personalized agents provide various services, including information provision, consultation, booking, cancellation, and complaint handling on social apps. On one hand, many products offer digital concierge services (e.g., HiJiffy), delivering automated services customized to suit diverse user needs, including customers’ queries, accents, emotions, preferences, and other characteristics. This reflects the brand’s commitment to superior service. On the other hand, travel agents are popular in many products (e.g., AI Adventures, Trava). These travel agents can pinpoint the travel needs of users and provide personalized services, including identifying popular destinations, grasping the underlying intentions behind user queries, and meet the emotional needs of customers. Through the analysis of collected user data, these products could refine their services and anticipate market shifts in tourism.

#### Customer Service

In customer service, personalized agents provide assistance to enhance problem-solving efficacy and user engagement. They offer 24/7 support across diverse domains and boost first-contact resolution rates. RPLAs leverage user feedback and implicit actions to continuously optimize their personalization and elevate the user experience (e.g., Ebi.Ai, boost.ai, Jason AI, Ada). Comprehensive AI assistants deliver and analyze user inquiries, preferences, and context to provide tailored responses. They also extract actionable insights from conversational data. For example, Viable targets businesses by empowering them with valuable understanding gleaned from large volumes of user feedback. This enables companies to make data-driven improvements to their products and services based on real customer needs and pain points.

#### Shopping

In the shopping industry, personalized agents simulate in-store conversations to provide tailored product recommendations, match items, and discover trends based on user preferences. Products such as Shopping Muse (e.g., Dynamic Yield by Mastercard) offer relevant product suggestions and help users find items that match their style and interests based on user preferences and needs through human-agent conversations.

#### Fitness

For fitness, personalized agents enhance the fitness training experience, both at home and in the gym. RPLA aims to play the role of coaches in setting realistic goals, adapting exercises based on progress and abilities, and providing multimodal feedback. Platforms such as WHOOP Coach and Humango collect users’ biometric information, physical characteristics, and fitness levels. Through natural language conversations, these agents offer personalized training plans tailored to individual preferences and needs. By making personalized health coaching more accessible, these AI-powered RPLAs democratize access to expert guidance and support for a wider audience.

#### Office

For office productivity, task-oriented RPLAs can role-play as the copilot for individual workers based on their office data, such as document files and code repositories. Hence, they deliver context-aware assistance for user requests, such as generating content and providing insights. For example, Microsoft 365 Copilot integrates various user data, to deliver intelligent service to respond to user queries and enable more convenient interaction with applications. It integrates with Microsoft Graph and utilizes user data from various sources, including documents, email threads and others, with continuous learning mechanisms to improve its performance over time. Similarly, GitHub Copilot integrates code repositories of individuals, and serves as the copilot to boost the productivity of programmers. These personalized RPLAs empower users to streamline their workflows and enhance productivity within the office environment.

Table 3: Overview of RPLA applications and products based on LLMs. For personalized data, “Personal Profile” refers to data about one’s identity, including age, appearance, voice, and biographical information. “Behavior History” denotes data derived from interactions between users and applications, representing user bahavior patterns. “File” pertains to documents and computer files, which contain private knowledge regardless of personal identity, such as code and manuals. The three types of personalized data roughly correspond to profile, interactions, and domain knowledge in §§\mathsection§[6](https://arxiv.org/html/2404.18231v1#S6 "6 Individualized Persona\(lization\) ‣ From Persona to Personalization: A Survey on Role-Playing Language Agents") respectively.  Product |  Domain |  Description |  Target Audience |  Generation Modality |  Personalized Data  
---|---|---|---|---|---  
Persona-oriented RPLA Products  
Character.ai |  Chatbots |  A general AI chat app with a wide range of characters based on individuals with specific professions or personalities |  ToC |  Text |  -  
Meta AI Familiar Faces |  Chatbots |  AI characters role-playing celebrities |  ToC |  Text |  -  
Hello History |  Chatbots |  Conversation with historical figures |  ToC |  Text |  -  
Chatfai |  Chatbots |  A general AI chat app with a wide range of characters based on individuals with specific professions or personalities |  ToC |  Text |  -  
Replika |  Chatbots |  An AI companion that serves as an empathetic friend to the user |  ToC |  Text |  Behavior History  
Rosebud |  Chatbots |  An AI friend that allows users to journal their thoughts for mental health and personal growth |  ToC |  Text |  Behavior History File  
Rewind |  Chatbots |  A personalized agent that has context of what users have seen, heard, or said on their device |  ToC/ToB |  Text Audio |  Personal Profile Behavior History File  
BHuman |  Chatbots |  AI digital clone of oneself with added modalities of face cloning and voice cloning |  ToC/ToB |  Text Audio Video |  Personal Profile Behavior History File  
personal.ai |  Chatbots |  Train one’s own AI with knowledge of oneself and their own memories |  ToC/ToB |  Text |  Personal Profile Behavior History File  
Ememe |  Games |  An AI NPC sandbox that allows users to create characters and observe their life and interactions |  ToC |  Text |  -  
AI Dungeon |  Games |  A text-based adventure game where users define the characters and the setting and also participate in the game as a character |  ToC |  Text |  -  
Saga |  Games |  An interactive fiction game where one can play as an character from pre-existing Worlds and Characters from popular franchises and media |  ToC |  Text |  -  
Hidden Door |  Games |  An interactive game that allows users to play as a character in a world that is converted from existing movie, novel or other types of franchise |  ToC |  Text |  -  
Product |  Domain |  Description |  Target Audience |  Generation Modality |  Personalized Data  
---|---|---|---|---|---  
Task-oriented RPLAs  
GPTs |  All |  GPTs from GPT Store are tailored version of ChatGPT for specific tasks developed by the ChatGPT community, with categories like image generation, writing, research, programming, education. |  ToC |  Text Image |  -  
Duolingo Max |  Education |  AI Agent that helps users to learn English better |  ToC |  Text |  Personal Profile Behavior History  
Jagoda.AI |  Education |  Personalized educational experience |  ToC |  Text |  Personal Profile File  
Squirrel AI |  Education |  Uses LLMs and AI for adaptive learning |  ToC |  Text |  -  
Eduaide.Ai |  Education |  Eduaide.ai uses AI to generate custom teaching resources and assessments from educator inputs, simplifying lesson planning in multiple languages. |  ToC |  Text |  -  
Squirrel AI |  Education |  SquirrelAI employs AI to personalize learning by analyzing student performance, adjusting content, and offering tailored resources and feedback. |  ToC |  Text |  -  
Autonomous HR Chatbot |  Human Resource |  An HR chatbot that automates interviews and uses Pinecone’s semantic search, powered by ChatGPT and GPT-3.5-turbo. |  ToC |  Text |  -  
Huru |  Human Resource |  Huru AI delivers personalized interview prep, featuring a Chrome Extension for real job listing practice and a mobile app for users on the move. |  ToC |  Text Video |  Personal Profile Behavior History  
Careers AI |  Human Resource |  A platform that provides career advice and planning, helping users to identify and achieve their career goals. |  ToC |  Text |  Personal Profile Behavior History  
Epique |  Real Estate |  Create a blog post, write a real estate property description, draft an account activation email, and develop Instagram content about legal service pricing. |  ToB |  Text |  -  
PropertyPen |  Real Estate |  Generates property listings, provides market analysis, and automates responses |  ToB |  Text |  -  
Listingcopy |  Real Estate |  AI tool for creating property listings and attractive content for real estate agents |  ToB |  Text |  Behavior data  
Ada |  Customer Service |  Ada.cx delivers personalized customer experiences across various industries, analyzing customer data like past interactions and purchases to anticipate needs and streamline interactions. |  ToB |  Text |  Behavior History File  
Ebi.Ai |  Customer Service |  AI assistant platform for business offering customer service and support |  ToB |  Text |  -  
Product |  Domain |  Description |  Target Audience |  Generation Modality |  Personalized Data  
---|---|---|---|---|---  
Task-oriented RPLAs  
Jason AI |  Customer Service |  AI assistant for B2B sales, enhancing lead generation and sales strategies. |  ToB |  Text |  Behavior History File  
Aide |  Customer Service |  Aide enhances customer experiences by analyzing conversations for insights, automating workflows, and boosting agent efficiency with AI. |  ToB |  Text |  -  
Zendesk AI |  Customer Service |  AI customer support agents |  ToB |  Text |  Personal Profile Behavior History File  
Air.ai |  Customer Service |  An AI sales and customer service agent that can perform a real phone call and take actions across applications |  ToB |  Audio |  Personal Profile Behavior History File  
boost.ai |  Customer Service |  Conversational AI platform for automating customer service and internal support using chat and voice chatbots. |  ToB |  Text Voice |  Personal Profile Behavior History  
Viable |  Customer Service |  Viable offers automated user feedback analysis for actionable business insights, customizing data processing to target improvements and inform strategic decisions. |  ToB |  Text |  -  
HyperWrite |  Content Generation |  An AI writing assistant that helps users in composing essays and other texts more confidently |  ToC |  Text |  Behavior History  
AI Story Generator |  Content Generation |  A tool for generating story ideas, helping writers overcome creative blocks. |  ToC |  Text |  Behavior History  
EZAi AI |  Content Generation |  An AI app for Android and IOS that helps users on generating high quality content for social media and Blogs |  ToC/ToB |  Text |  Behavior History  
AI Majic |  Content Generation |  AI that specializes in creating and managing social media content and Blogs |  ToC/ToB |  Text |  Behavior History  
Jasper |  Content Generation |  Personalized writing suggestions |  ToB |  Text |  Personal Profile Behavior History  
ShortlyAI |  Content Generation |  Personalized content generation |  ToC |  Text |  Behavior History  
IBM Watson Health |  Health |  AI Agent that provides hidden health problems with personalized plan |  ToC |  Text |  -  
Babylon Health |  Health |  LLMs process de-identified medical data with consent, personalizing healthcare through triage, diagnosis, and health predictions. |  ToC |  Text |  -  
Product |  Domain |  Description |  Target Audience |  Generation Modality |  Personalized Data  
---|---|---|---|---|---  
Task-oriented RPLAs  
AI Adventures |  Travel |  Use LLM and external tools (API calls) to give personalized plan on travel plans |  ToC |  Text |  Behavior History  
Trava |  Travel |  AI travel assistant that facilitates travel bookings and itinerary management. |  ToC |  Text |  Personal Profile  
Shopping Muse |  Shopping |  Shopping Muse by Mastercard offers a tailored online shopping experience, simulating in-store conversations to recommend products, match items, and discover trends based on user preferences. |  ToC |  Text |  Personal Profile Behavior History File  
WHOOP Coach |  Fitness |  Give advice and responses for fitness goals/plans uniquely tailored to users’ biometric data |  ToC |  Text |  Personal Profile Behavior History File  
Humango |  Fitness |  Give customized workout plans and engaging in conversational interactions |  ToC |  Text |  Personal Profile Behavior History File  
Microsoft Copilot |  Office |  Integration into Microsoft 365 apps like Word, Excel, PowerPoint, Outlook, Teams for enhanced creativity and productivity. |  ToC |  Text |  Personal Profile Behavior History File  
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url: http://arxiv.org/abs/1706.06987v1
title: [1706.06987v1] A Generative Model of Group Conversation
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# Computer Science > Computation and Language

**arXiv:1706.06987v1** (cs) 

[Submitted on 21 Jun 2017]

# Title:A Generative Model of Group Conversation

Authors:[Hannah Morrison](https://arxiv.org/search/cs?searchtype=author&query=Morrison,+H), [Chris Martens](https://arxiv.org/search/cs?searchtype=author&query=Martens,+C)

View a PDF of the paper titled A Generative Model of Group Conversation, by Hannah Morrison and 1 other authors

[View PDF](/pdf/1706.06987v1)

> Abstract:Conversations with non-player characters (NPCs) in games are typically confined to dialogue between a human player and a virtual agent, where the conversation is initiated and controlled by the player. To create richer, more believable environments for players, we need conversational behavior to reflect initiative on the part of the NPCs, including conversations that include multiple NPCs who interact with one another as well as the player. We describe a generative computational model of group conversation between agents, an abstract simulation of discussion in a small group setting. We define conversational interactions in terms of rules for turn taking and interruption, as well as belief change, sentiment change, and emotional response, all of which are dependent on agent personality, context, and relationships. We evaluate our model using a parameterized expressive range analysis, observing correlations between simulation parameters and features of the resulting conversations. This analysis confirms, for example, that character personalities will predict how often they speak, and that heterogeneous groups of characters will generate more belief change. 

Comments: | Accepted submission for the Workshop on Non-Player Characters and Social Believability in Games at FDG 2017  
---|---  
Subjects: |  Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)  
Cite as: | [arXiv:1706.06987](https://arxiv.org/abs/1706.06987) [cs.CL]  
  | (or  [arXiv:1706.06987v1](https://arxiv.org/abs/1706.06987v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.1706.06987> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Hannah Morrison [[view email](/show-email/aed8be51/1706.06987)]   
**[v1]** Wed, 21 Jun 2017 16:19:59 UTC (81 KB)  


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url: https://arxiv.org/abs/2502.19519
title: [2502.19519] Static Vs. Agentic Game Master AI for Facilitating Solo Role-Playing Experiences
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# Computer Science > Human-Computer Interaction

**arXiv:2502.19519** (cs) 

[Submitted on 26 Feb 2025 ([v1](https://arxiv.org/abs/2502.19519v1)), last revised 6 Mar 2025 (this version, v2)]

# Title:Static Vs. Agentic Game Master AI for Facilitating Solo Role-Playing Experiences

Authors:[Nicolai Hejlesen Jørgensen](https://arxiv.org/search/cs?searchtype=author&query=J%C3%B8rgensen,+N+H), [Sarmilan Tharmabalan](https://arxiv.org/search/cs?searchtype=author&query=Tharmabalan,+S), [Ilhan Aslan](https://arxiv.org/search/cs?searchtype=author&query=Aslan,+I), [Nicolai Brodersen Hansen](https://arxiv.org/search/cs?searchtype=author&query=Hansen,+N+B), [Timothy Merritt](https://arxiv.org/search/cs?searchtype=author&query=Merritt,+T)

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> Abstract:This paper presents a game master AI for single-player role-playing games. The AI is designed to deliver interactive text-based narratives and experiences typically associated with multiplayer tabletop games like Dungeons & Dragons. We report on the design process and the series of experiments to improve the functionality and experience design, resulting in two functional versions of the system. While v1 of our system uses simplified prompt engineering, v2 leverages a multi-agent architecture and the ReAct framework to include reasoning and action. A comparative evaluation demonstrates that v2 as an agentic system maintains play while significantly improving modularity and game experience, including immersion and curiosity. Our findings contribute to the evolution of AI-driven interactive fiction, highlighting new avenues for enhancing solo role-playing experiences. 

Comments: | 17 pages, 10 figures, 1 table, submitted for review  
---|---  
Subjects: |  Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:2502.19519](https://arxiv.org/abs/2502.19519) [cs.HC]  
  | (or  [arXiv:2502.19519v2](https://arxiv.org/abs/2502.19519v2) [cs.HC] for this version)   
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## Submission history

From: Timothy Merritt [[view email](/show-email/9c05d2ff/2502.19519)]   
**[[v1]](/abs/2502.19519v1)** Wed, 26 Feb 2025 19:42:22 UTC (12,011 KB)  
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# Using Large Language Models for NPC conversations in game development

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In game development and the engineering of game engines or platforms, the subject of NPC (non-player character) interaction and dialogue systems inevitably arises as a critical focus. Delivering a seamless and engaging player experience hinges significantly on the quality of interaction between player characters and NPCs.

For many game genres, these interactions extend beyond mere mechanics and into the domain of meaningful conversations. Whether your project involves creating an immersive RPG, a story-driven adventure, or a simulation game, the ability for player characters to communicate with NPCs can serve as a cornerstone of gameplay. Dialogue systems must not only be intuitive but also contextually rich to enhance narrative depth and player engagement.

Moreover, the design of NPC interaction systems demands careful consideration of the underlying technology. Factors such as dynamic response generation, branching dialogue trees, and AI-driven character behavior are pivotal in achieving realism and interactivity. These elements ensure that NPCs respond in a manner that feels natural and adaptive to the player’s choices, thereby elevating the overall game experience.

As gaming audiences continue to expect more sophisticated storytelling and interactivity, the importance of…

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##  Test Quest : Improving the onboarding experience for novice Dungeons and Dragons players through personalised AI-generated content. 

Andrei, Luca (2024) _Test Quest : Improving the onboarding experience for novice Dungeons and Dragons players through personalised AI-generated content._

[![\[img\]](http://essay.utwente.nl/100744/1.hassmallThumbnailVersion/Andrei_BA_EEMCS.pdf)](http://essay.utwente.nl/100744/1/Andrei_BA_EEMCS.pdf)|  [PDF](http://essay.utwente.nl/100744/1/Andrei_BA_EEMCS.pdf)   
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Abstract:| This thesis explores the development of an AI-based tool designed to lower the entry barriers for new players of Dungeons & Dragons (D&D), a tabletop role-playing game that has experienced consistent growth over more than three decades. This project leverages a Large Language Model to simplify the onboarding process for new players, enabling a more seamless introduction to the game. The tool developed through this thesis assists in narrative and content creation tailored to the specific characteristics and preferences of player groups, facilitating engagement in scenarios that align with their learning objectives. To evaluate the system, we conducted a mixed-methods evaluation involving both qualitative feedback from focus groups and quantitative data analysis to assess the effectiveness of the system. Results indicate that the automated materials might help reduce preparation time and support the initial gaming experience by providing coherent, engaging, and highly customizable content. The system was particularly effective in helping new players create customised characters with ease and in boosting confidence levels among players with low experience. The findings may also have implications for the design of other complex games and educational frameworks, where AI can play a role in reducing entry barriers and enhancing learning processes.  
---|---  
Item Type:| Essay (Bachelor)  
Faculty:| EEMCS: Electrical Engineering, Mathematics and Computer Science  
Subject:| 01 general works, 02 science and culture in general, 18 languages and literature, 50 technical science in general, 54 computer science, 58 process technology, 76 recreation, leisure, 81 education, teaching  
Programme:| Creative Technology BSc (50447)  
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# Customizing Experiences for Mobile Virtual Reality

dos Santos Gomes Agrela, José Gabriel

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.  Universidade da Madeira (Portugal) ProQuest Dissertations & Theses, 2023. 31055835. 

  


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### [ Faithful Persona-based Conversational Dataset Generation with Large Language Models ](/papers/2312.10007)

[High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations.](/papers/2312.10007)

[

  *   *   * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/mRebsJ0VluWWoGhuN6jUb.jpeg)
  * ![](/avatars/20f95ed65cc321f03f2c510ff1ab9b61.svg)
  * ![](/avatars/75ace8f1e9ce516fc4543e6c11bbe6e7.svg)
  * 5 authors 

· Dec 15, 2023](/papers/2312.10007) [ 1](/papers/2312.10007#community)

[ 1](/login?next=%2Fpapers%2F2305.17388)

### [ MPCHAT: Towards Multimodal Persona-Grounded Conversation ](/papers/2305.17388)

[In order to build self-consistent personalized dialogue agents, previous research has mostly focused on textual persona that delivers personal facts or personalities. However, to fully describe the multi-faceted nature of persona, image modality can help better reveal the speaker's personal characteristics and experiences in episodic memory (Rubin et al., 2003; Conway, 2009). In this work, we extend persona-based dialogue to the multimodal domain and make two main contributions. First, we present the first multimodal persona-based dialogue dataset named MPCHAT, which extends persona with both text and images to contain episodic memories. Second, we empirically show that incorporating multimodal persona, as measured by three proposed multimodal persona-grounded dialogue tasks (i.e., next response prediction, grounding persona prediction, and speaker identification), leads to statistically significant performance improvements across all tasks. Thus, our work highlights that multimodal persona is crucial for improving multimodal dialogue comprehension, and our MPCHAT serves as a high-quality resource for this research.](/papers/2305.17388)

[

  *   *   *   * ![](/avatars/038894bc72b92ec3f4ecb096cc60b60a.svg)
  * 4 authors 

· May 27, 2023](/papers/2305.17388)

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### [ P5: Plug-and-Play Persona Prompting for Personalized Response Selection ](/papers/2310.06390)

[The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available. We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data. This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus. Additionally, our model can be fine-tuned for even better performance. In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively. The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively. To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences. Our code is available on github~https://github.com/rungjoo/plug-and-play-prompt-persona.](/papers/2310.06390)

[

  *   *   *   * 3 authors 

· Oct 10, 2023](/papers/2310.06390)

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### [ PAL: Persona-Augmented Emotional Support Conversation Generation ](/papers/2212.09235)

[Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that PAL achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.](/papers/2212.09235)

[

  *   *   *   * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/60ab811508805574fcf076ec/ofqwh2wbTPcxjol6ooDUd.jpeg)
  * ![](/avatars/805c5f909f52656345b8bde486c9fa8f.svg)
  * 5 authors 

· Dec 18, 2022](/papers/2212.09235)

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### [ Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization ](/papers/2406.01171)

[The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., personalized search, LLM-as-a-judge). However, the growing research on leveraging persona in LLMs is relatively disorganized and lacks a systematic taxonomy. To close the gap, we present a comprehensive survey to categorize the current state of the field. We identify two lines of research, namely (1) LLM Role-Playing, where personas are assigned to LLMs, and (2) LLM Personalization, where LLMs take care of user personas. Additionally, we introduce existing methods for LLM personality evaluation. To the best of our knowledge, we present the first survey for role-playing and personalization in LLMs under the unified view of persona. We continuously maintain a paper collection to foster future endeavors: https://github.com/MiuLab/PersonaLLM-Survey](/papers/2406.01171)

[

  *   *   *   * ![](/avatars/afa58f39896c5caef512675450c7d6ce.svg)
  * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/65e0538c527a7da472310059/-w7vfNyHOpym7b57WWhhT.jpeg)
  * 7 authors 

· Jun 3, 2024](/papers/2406.01171)

[ -](/login?next=%2Fpapers%2F2308.10278)

### [ CharacterChat: Learning towards Conversational AI with Personalized Social Support ](/papers/2308.10278)

[In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency. However, traditional methods such as Emotional Support Conversations (ESC) face challenges in effectively addressing a diverse range of individual personalities. In response, we introduce the Social Support Conversation (S2Conv) framework. It comprises a series of support agents and the interpersonal matching mechanism, linking individuals with persona-compatible virtual supporters. Utilizing persona decomposition based on the MBTI (Myers-Briggs Type Indicator), we have created the MBTI-1024 Bank, a group that of virtual characters with distinct profiles. Through improved role-playing prompts with behavior preset and dynamic memory, we facilitate the development of the MBTI-S2Conv dataset, which contains conversations between the characters in the MBTI-1024 Bank. Building upon these foundations, we present CharacterChat, a comprehensive S2Conv system, which includes a conversational model driven by personas and memories, along with an interpersonal matching plugin model that dispatches the optimal supporters from the MBTI-1024 Bank for individuals with specific personas. Empirical results indicate the remarkable efficacy of CharacterChat in providing personalized social support and highlight the substantial advantages derived from interpersonal matching. The source code is available in https://github.com/morecry/CharacterChat.](/papers/2308.10278)

[

  *   *   *   *   *   * 8 authors 

· Aug 20, 2023](/papers/2308.10278)

[ -](/login?next=%2Fpapers%2F2412.09034)

### [ Dialogue Language Model with Large-Scale Persona Data Engineering ](/papers/2412.09034)

[Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limited scale and diversity of current persona dialogue datasets remain challenges to achieving robust persona-consistent dialogue models. In this study, drawing inspiration from the success of large-scale pre-training, we introduce PPDS, an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialogue dataset to enhance persona consistency. Specifically, we present a persona extraction model designed to autonomously and precisely generate vast persona dialogue datasets. Additionally, we unveil a pioneering persona augmentation technique to address the invalid persona bias inherent in the constructed dataset. Both quantitative and human evaluations consistently highlight the superior response quality and persona consistency of our proposed model, underscoring its effectiveness.](/papers/2412.09034)

[

  *   *   *   *   *   * 5 authors 

· Dec 12, 2024](/papers/2412.09034)

[ -](/login?next=%2Fpapers%2F1911.04700)

### [ A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data ](/papers/1911.04700)

[Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.](/papers/1911.04700)

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  *   *   *   *   * 4 authors 

· Nov 12, 2019](/papers/1911.04700)

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### [ Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems ](/papers/2310.05280)

[Recent advancements in Large Language Models empower them to follow freeform instructions, including imitating generic or specific demographic personas in conversations. We define generic personas to represent demographic groups, such as "an Asian person", whereas specific personas may take the form of specific popular Asian names like "Yumi". While the adoption of personas enriches user experiences by making dialogue systems more engaging and approachable, it also casts a shadow of potential risk by exacerbating social biases within model responses, thereby causing societal harm through interactions with users. In this paper, we systematically study "persona biases", which we define to be the sensitivity of dialogue models' harmful behaviors contingent upon the personas they adopt. We categorize persona biases into biases in harmful expression and harmful agreement, and establish a comprehensive evaluation framework to measure persona biases in five aspects: Offensiveness, Toxic Continuation, Regard, Stereotype Agreement, and Toxic Agreement. Additionally, we propose to investigate persona biases by experimenting with UNIVERSALPERSONA, a systematically constructed persona dataset encompassing various types of both generic and specific model personas. Through benchmarking on four different models -- including Blender, ChatGPT, Alpaca, and Vicuna -- our study uncovers significant persona biases in dialogue systems. Our findings also underscore the pressing need to revisit the use of personas in dialogue agents to ensure safe application.](/papers/2310.05280)

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  *   *   * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/64c13de54cbd12e168f09a58/Aw5BaNzf6uuwqQ333OQ5y.png)
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  * ![](/avatars/1a9e6eb47c14fe278b8e1d907d518f46.svg)
  * 5 authors 

· Oct 8, 2023](/papers/2310.05280)

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### [ Toxicity in ChatGPT: Analyzing Persona-assigned Language Models ](/papers/2304.05335)

[Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with critical information needs like students or patients engaging with chatbots, the safety of these systems is of prime importance. Therefore, a clear understanding of the capabilities and limitations of LLMs is necessary. To this end, we systematically evaluate toxicity in over half a million generations of ChatGPT, a popular dialogue-based LLM. We find that setting the system parameter of ChatGPT by assigning it a persona, say that of the boxer Muhammad Ali, significantly increases the toxicity of generations. Depending on the persona assigned to ChatGPT, its toxicity can increase up to 6x, with outputs engaging in incorrect stereotypes, harmful dialogue, and hurtful opinions. This may be potentially defamatory to the persona and harmful to an unsuspecting user. Furthermore, we find concerning patterns where specific entities (e.g., certain races) are targeted more than others (3x more) irrespective of the assigned persona, that reflect inherent discriminatory biases in the model. We hope that our findings inspire the broader AI community to rethink the efficacy of current safety guardrails and develop better techniques that lead to robust, safe, and trustworthy AI systems.](/papers/2304.05335)

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  *   *   *   *   *   * 5 authors 

· Apr 11, 2023](/papers/2304.05335)

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### [ Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona ](/papers/2305.11482)

[The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three resources: sparse or dense persona descriptions and dialogue histories. However, sparse structured persona attributes are explicit but uninformative, dense persona texts contain rich persona descriptions with much noise, and dialogue history query is both noisy and uninformative for persona modeling. In this work, we combine the advantages of the three resources to obtain a richer and more accurate persona. We design a Contrastive Latent Variable-based model (CLV) that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses. Experimental results on Chinese and English datasets demonstrate our model's superiority in personalization.](/papers/2305.11482)

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  *   *   *   *   * ![](/avatars/70258e03f9cb902b51bafefa5549e0be.svg)
  * 7 authors 

· May 19, 2023](/papers/2305.11482)

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### [ PersonaGym: Evaluating Persona Agents and LLMs ](/papers/2407.18416)

[Persona agents, which are LLM agents that act according to an assigned persona, have demonstrated impressive contextual response capabilities across various applications. These persona agents offer significant enhancements across diverse sectors, such as education, healthcare, and entertainment, where model developers can align agent responses to different user requirements thereby broadening the scope of agent applications. However, evaluating persona agent performance is incredibly challenging due to the complexity of assessing persona adherence in free-form interactions across various environments that are relevant to each persona agent. We introduce PersonaGym, the first dynamic evaluation framework for assessing persona agents, and PersonaScore, the first automated human-aligned metric grounded in decision theory for comprehensive large-scale evaluation of persona agents. Our evaluation of 6 open and closed-source LLMs, using a benchmark encompassing 200 personas and 10,000 questions, reveals significant opportunities for advancement in persona agent capabilities across state-of-the-art models. For example, Claude 3.5 Sonnet only has a 2.97% relative improvement in PersonaScore than GPT 3.5 despite being a much more advanced model. Importantly, we find that increased model size and complexity do not necessarily imply enhanced persona agent capabilities thereby highlighting the pressing need for algorithmic and architectural invention towards faithful and performant persona agents.](/papers/2407.18416)

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  *   *   *   *   *   * 9 authors 

· Jul 25, 2024](/papers/2407.18416)

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### [ Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations ](/papers/2412.11250)

[Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author's personality. We further refine the data by capturing the Big Five personality traits --openness, conscientiousness, extraversion, agreeableness, and neuroticism --ensuring that dialogues authentically reflect an individual's personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11% improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues.](/papers/2412.11250)

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  *   *   * ![](/avatars/d249ba2ad9e4975c4f10d694030e38fb.svg)
  * 3 authors 

· Dec 15, 2024](/papers/2412.11250)

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### [ Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions ](/papers/2010.03205)

[Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly. For example, state-of-the-art models cannot infer that interest in hiking might imply love for nature or longing for a break. In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions. Additionally, we introduce fine-grained grounding on personas by encouraging the model to make a discrete choice among persona sentences while synthesizing a dialog response. Since such a choice is not observed in the data, we model it using a discrete latent random variable and use variational learning to sample from hundreds of persona expansions. Our model outperforms competitive baselines on the PersonaChat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation.](/papers/2010.03205)

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  *   *   *   * ![](/avatars/f618180951083af62522f7fb137d645a.svg)
  * 4 authors 

· Oct 7, 2020](/papers/2010.03205)

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### [ Dr. Jekyll and Mr. Hyde: Two Faces of LLMs ](/papers/2312.03853)

[Recently, we have witnessed a rise in the use of Large Language Models (LLMs), especially in applications like chatbot assistants. Safety mechanisms and specialized training procedures are implemented to prevent improper responses from these assistants. In this work, we bypass these measures for ChatGPT and Gemini (and, to some extent, Bing chat) by making them impersonate complex personas with personality characteristics that are not aligned with a truthful assistant. We start by creating elaborate biographies of these personas, which we then use in a new session with the same chatbots. Our conversations then follow a role-play style to elicit prohibited responses. Using personas, we show that prohibited responses are actually provided, making it possible to obtain unauthorized, illegal, or harmful information. This work shows that by using adversarial personas, one can overcome safety mechanisms set out by ChatGPT and Gemini. We also introduce several ways of activating such adversarial personas, which show that both chatbots are vulnerable to this kind of attack. With the same principle, we introduce two defenses that push the model to interpret trustworthy personalities and make it more robust against such attacks.](/papers/2312.03853)

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  *   *   *   *   *   * 5 authors 

· Dec 6, 2023](/papers/2312.03853)

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### [ Improving Personality Consistency in Conversation by Persona Extending ](/papers/2208.10816)

[Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate responses in light of static predefined personas depicted with textual description, which may severely restrict the interactivity of human and the chatbot, especially when the agent needs to answer the query excluded in the predefined personas, which is so-called out-of-predefined persona problem (named OOP for simplicity). To alleviate the problem, in this paper we propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, (1) Persona Retrieval Model (PRM), it retrieves a persona from a global collection based on a Natural Language Inference (NLI) model, the inferred persona is consistent with the predefined personas; and (2) Posterior-scored Transformer (PS-Transformer), it adopts a persona posterior distribution that further considers the actual personas used in the ground response, maximally mitigating the gap between training and inferring. Furthermore, we present a dataset called IT-ConvAI2 that first highlights the OOP problem in personalized dialogue. Extensive experiments on both IT-ConvAI2 and ConvAI2 demonstrate that our proposed model yields considerable improvements in both automatic metrics and human evaluations.](/papers/2208.10816)

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  *   *   *   *   *   * 6 authors 

· Aug 23, 2022](/papers/2208.10816)

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### [ A Personalized Dialogue Generator with Implicit User Persona Detection ](/papers/2204.07372)

[Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a large number of detailed personas for each user, we attempted to model the user's potential persona and its representation from dialogue history, with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people being aware of each other's persona and producing a corresponding expression in conversation. Finally, posterior-discriminated regularization was presented to enhance the training procedure. Empirical studies demonstrate that, compared to state-of-the-art methods, our approach is more concerned with the user's persona and achieves a considerable boost across the evaluations.](/papers/2204.07372)

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  *   *   *   *   * 4 authors 

· Apr 15, 2022](/papers/2204.07372)

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### [ What makes a good conversation? How controllable attributes affect human judgments ](/papers/1902.08654)

[A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.](/papers/1902.08654)

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  *   *   *   *   * 4 authors 

· Feb 22, 2019](/papers/1902.08654)

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### [ Personalized Dialogue Generation with Diversified Traits ](/papers/1901.09672)

[Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.](/papers/1901.09672)

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  *   *   *   *   *   * 5 authors 

· Jan 28, 2019](/papers/1901.09672)

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### [ CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models ](/papers/2402.15265)

[Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this research, we investigated how users customize agent personas and their impact on interaction quality, diversity, and dynamics. To this end, we developed CloChat, an interface supporting easy and accurate customization of agent personas in LLMs. We conducted a study comparing how participants interact with CloChat and ChatGPT. The results indicate that participants formed emotional bonds with the customized agents, engaged in more dynamic dialogues, and showed interest in sustaining interactions. These findings contribute to design implications for future systems with conversational agents using LLMs.](/papers/2402.15265)

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  *   *   *   *   *   * 5 authors 

· Feb 23, 2024](/papers/2402.15265)

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### [ Large Language Model as a User Simulator ](/papers/2308.11534)

[The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT conversations, as evidenced by Vicuna. However, while current endeavors like Baize and UltraChat aim to auto-generate conversational data due to challenges in gathering human participation, they primarily rely on ChatGPT to simulate human behaviors based on directives rather than genuine human learning. This results in a limited scope, diminished diversity, and an absence of genuine multi-round conversational dynamics. To address the above issues, we innovatively target human questions extracted from genuine human-machine conversations as a learning goal and train a user simulator, UserGPT, to produce a high-quality human-centric synthetic conversation dataset, RealChat. Subsequently, this dataset trains our assistant model, ReaLM. Experimentally, ReaLM outpaces baseline models in both Vicuna-Bench and MT-Bench by pairwise comparison when considering equivalent training set sizes, and manual evaluation also shows that our model is highly competitive. Impressively, when fine-tuned with the latest LLaMA 2 model, ReaLM secured a leading score of 6.33 in the MT-Bench, outshining the contemporary same-scale models, including the LLaMA-2-7B-chat model. Further in-depth analysis demonstrates the scalability and transferability of our approach. A preliminary exploration into the interplay between training set data quality and resultant model performance is also undertaken, laying a robust groundwork for future investigations. The code is available at https://github.com/FreedomIntelligence/ReaLM.](/papers/2308.11534)

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  * 5 authors 

· Aug 21, 2023](/papers/2308.11534)

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### [ From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations ](/papers/2502.11451)

[The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can infer core persona traits, 2) subtle shifts in emotionality and extraversion occur, influencing the dialogue dynamics, and 3) the application of persona traits modifies the distribution of emotional support strategies, enhancing the relevance and empathetic quality of the responses. These findings highlight the potential of persona-driven LLMs in crafting more personalized, empathetic, and effective emotional support dialogues, which has significant implications for the future design of AI-driven emotional support systems.](/papers/2502.11451)

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  *   *   *   *   *   * 5 authors 

· Feb 17](/papers/2502.11451)

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### [ Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation ](/papers/2311.03348)

[Despite efforts to align large language models to produce harmless responses, they are still vulnerable to jailbreak prompts that elicit unrestricted behaviour. In this work, we investigate persona modulation as a black-box jailbreaking method to steer a target model to take on personalities that are willing to comply with harmful instructions. Rather than manually crafting prompts for each persona, we automate the generation of jailbreaks using a language model assistant. We demonstrate a range of harmful completions made possible by persona modulation, including detailed instructions for synthesising methamphetamine, building a bomb, and laundering money. These automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is 185 times larger than before modulation (0.23%). These prompts also transfer to Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%, respectively. Our work reveals yet another vulnerability in commercial large language models and highlights the need for more comprehensive safeguards.](/papers/2311.03348)

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  * 6 authors 

· Nov 6, 2023](/papers/2311.03348)

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### [ From Persona to Personalization: A Survey on Role-Playing Language Agents ](/papers/2404.18231)

[Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, including in-context learning, instruction following, and social intelligence, RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. RPLAs can mimic a wide range of personas, ranging from historical figures and fictional characters to real-life individuals. Consequently, they have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots, and digital clones. In this paper, we conduct a comprehensive survey of this field, illustrating the evolution and recent progress in RPLAs integrating with cutting-edge LLM technologies. We categorize personas into three types: 1) Demographic Persona, which leverages statistical stereotypes; 2) Character Persona, focused on well-established figures; and 3) Individualized Persona, customized through ongoing user interactions for personalized services. We begin by presenting a comprehensive overview of current methodologies for RPLAs, followed by the details for each persona type, covering corresponding data sourcing, agent construction, and evaluation. Afterward, we discuss the fundamental risks, existing limitations, and future prospects of RPLAs. Additionally, we provide a brief review of RPLAs in AI applications, which reflects practical user demands that shape and drive RPLA research. Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.](/papers/2404.18231)

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  * 18 authors 

· Apr 28, 2024](/papers/2404.18231)

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### [ Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement ](/papers/2401.14215)

[Memorizing and utilizing speakers' personas is a common practice for response generation in long-term conversations. Yet, human-authored datasets often provide uninformative persona sentences that hinder response quality. This paper presents a novel framework that leverages commonsense-based persona expansion to address such issues in long-term conversation. While prior work focuses on not producing personas that contradict others, we focus on transforming contradictory personas into sentences that contain rich speaker information, by refining them based on their contextual backgrounds with designed strategies. As the pioneer of persona expansion in multi-session settings, our framework facilitates better response generation via human-like persona refinement. The supplementary video of our work is available at https://caffeine-15bbf.web.app/.](/papers/2401.14215)

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  * 5 authors 

· Jan 25, 2024](/papers/2401.14215)

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### [ Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues ](/papers/2301.04871)

[Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is available at https://github.com/Chenrj233/LMEDR.](/papers/2301.04871)

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  *   *   *   *   * 4 authors 

· Jan 12, 2023](/papers/2301.04871)

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### [ You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona ](/papers/2301.02401)

[To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever's effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at https://github.com/dlawjddn803/INFO](/papers/2301.02401)

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  * 11 authors 

· Jan 6, 2023](/papers/2301.02401)

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### [ PsyPlay: Personality-Infused Role-Playing Conversational Agents ](/papers/2502.03821)

[The current research on Role-Playing Conversational Agents (RPCAs) with Large Language Models (LLMs) primarily focuses on imitating specific speaking styles and utilizing character backgrounds, neglecting the depiction of deeper personality traits.~In this study, we introduce personality-infused role-playing for LLM agents, which encourages agents to accurately portray their designated personality traits during dialogues. We then propose PsyPlay, a dialogue generation framework that facilitates the expression of rich personalities among multiple LLM agents. Specifically, PsyPlay enables agents to assume roles with distinct personality traits and engage in discussions centered around specific topics, consistently exhibiting their designated personality traits throughout the interactions. Validation on generated dialogue data demonstrates that PsyPlay can accurately portray the intended personality traits, achieving an overall success rate of 80.31% on GPT-3.5. Notably, we observe that LLMs aligned with positive values are more successful in portraying positive personality roles compared to negative ones. Moreover, we construct a dialogue corpus for personality-infused role-playing, called PsyPlay-Bench. The corpus, which consists of 4745 instances of correctly portrayed dialogues using PsyPlay, aims to further facilitate research in personalized role-playing and dialogue personality detection.](/papers/2502.03821)

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  *   *   *   *   *   * 5 authors 

· Feb 6](/papers/2502.03821)

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### [ REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation ](/papers/2502.13270)

[Long-term, open-domain dialogue capabilities are essential for chatbots aiming to recall past interactions and demonstrate emotional intelligence (EI). Yet, most existing research relies on synthetic, LLM-generated data, leaving open questions about real-world conversational patterns. To address this gap, we introduce REALTALK, a 21-day corpus of authentic messaging app dialogues, providing a direct benchmark against genuine human interactions. We first conduct a dataset analysis, focusing on EI attributes and persona consistency to understand the unique challenges posed by real-world dialogues. By comparing with LLM-generated conversations, we highlight key differences, including diverse emotional expressions and variations in persona stability that synthetic dialogues often fail to capture. Building on these insights, we introduce two benchmark tasks: (1) persona simulation where a model continues a conversation on behalf of a specific user given prior dialogue context; and (2) memory probing where a model answers targeted questions requiring long-term memory of past interactions. Our findings reveal that models struggle to simulate a user solely from dialogue history, while fine-tuning on specific user chats improves persona emulation. Additionally, existing models face significant challenges in recalling and leveraging long-term context within real-world conversations.](/papers/2502.13270)

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  * 5 authors 

· Feb 18](/papers/2502.13270) [ 2](/papers/2502.13270#community)

[ 3](/login?next=%2Fpapers%2F2502.18968)

### [ Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles ](/papers/2502.18968)

[User simulators are crucial for replicating human interactions with dialogue systems, supporting both collaborative training and automatic evaluation, especially for large language models (LLMs). However, existing simulators often rely solely on text utterances, missing implicit user traits such as personality, speaking style, and goals. In contrast, persona-based methods lack generalizability, as they depend on predefined profiles of famous individuals or archetypes. To address these challenges, we propose User Simulator with implicit Profiles (USP), a framework that infers implicit user profiles from human-machine conversations and uses them to generate more personalized and realistic dialogues. We first develop an LLM-driven extractor with a comprehensive profile schema. Then, we refine the simulation through conditional supervised fine-tuning and reinforcement learning with cycle consistency, optimizing it at both the utterance and conversation levels. Finally, we adopt a diverse profile sampler to capture the distribution of real-world user profiles. Experimental results demonstrate that USP outperforms strong baselines in terms of authenticity and diversity while achieving comparable performance in consistency. Furthermore, dynamic multi-turn evaluations based on USP strongly align with mainstream benchmarks, demonstrating its effectiveness in real-world applications.](/papers/2502.18968)

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  * 6 authors 

· Feb 26](/papers/2502.18968) [ 3](/papers/2502.18968#community)

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### [ Open-Source Large Language Models as Multilingual Crowdworkers: Synthesizing Open-Domain Dialogues in Several Languages With No Examples in Targets and No Machine Translation ](/papers/2503.03462)

[The prevailing paradigm in the domain of Open-Domain Dialogue agents predominantly focuses on the English language, encompassing both models and datasets. Furthermore, the financial and temporal investments required for crowdsourcing such datasets for finetuning are substantial, particularly when multiple languages are involved. Fortunately, advancements in Large Language Models (LLMs) have unveiled a plethora of possibilities across diverse tasks. Specifically, instruction-tuning has enabled LLMs to execute tasks based on natural language instructions, occasionally surpassing the performance of human crowdworkers. Additionally, these models possess the capability to function in various languages within a single thread. Consequently, to generate new samples in different languages, we propose leveraging these capabilities to replicate the data collection process. We introduce a pipeline for generating Open-Domain Dialogue data in multiple Target Languages using LLMs, with demonstrations provided in a unique Source Language. By eschewing explicit Machine Translation in this approach, we enhance the adherence to language-specific nuances. We apply this methodology to the PersonaChat dataset. To enhance the openness of generated dialogues and mimic real life scenarii, we added the notion of speech events corresponding to the type of conversation the speakers are involved in and also that of common ground which represents the premises of a conversation.](/papers/2503.03462)

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· Mar 5](/papers/2503.03462)

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### [ XPersona: Evaluating Multilingual Personalized Chatbot ](/papers/2003.07568)

[Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines, and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.](/papers/2003.07568)

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  * 8 authors 

· Mar 17, 2020](/papers/2003.07568)

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### [ MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control ](/papers/2310.18342)

[Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models. However, textual personas are limited in describing multi-faceted attributes (e.g., language style, inner character nuances), implicit embedding suffers from personality sparsity, and handicraft prompts lack fine-grained and stable controllability. Hence, these approaches may struggle with complex personalized dialogue generation tasks that require generating controllable responses with multiple personal attributes. To this end, we propose \textsc{Miracle}, a novel personalized dialogue generation method through MultIple PeRsonal Attributes Control within Latent-Space Energy-based Models. ttributes Control within Latent-Space Energy-based Models. Specifically, our approach first disentangles complex personality into multi-faceted attributes. Subsequently, we employ a conditional variational auto-encoder to align with the dense personalized responses within a latent joint attribute space. We have also tailored a dedicated energy function and customized the ordinary differential equations sampling method to offer flexible attribute composition and precise attribute control. Extensive experiments demonstrate that Miracle outperforms several strong baselines in terms of personality controllability and response generation quality. Our dataset and code are available at https://github.com/LZY-the-boys/MIRACLE](/papers/2310.18342)

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  * 6 authors 

· Oct 22, 2023](/papers/2310.18342)

[ 62](/login?next=%2Fpapers%2F2405.01470)

### [ WildChat: 1M ChatGPT Interaction Logs in the Wild ](/papers/2405.01470)

[Chatbots such as GPT-4 and ChatGPT are now serving millions of users. Despite their widespread use, there remains a lack of public datasets showcasing how these tools are used by a population of users in practice. To bridge this gap, we offered free access to ChatGPT for online users in exchange for their affirmative, consensual opt-in to anonymously collect their chat transcripts and request headers. From this, we compiled WildChat, a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns. We compare WildChat with other popular user-chatbot interaction datasets, and find that our dataset offers the most diverse user prompts, contains the largest number of languages, and presents the richest variety of potentially toxic use-cases for researchers to study. In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses, alongside request headers. This augmentation allows for more detailed analysis of user behaviors across different geographical regions and temporal dimensions. Finally, because it captures a broad range of use cases, we demonstrate the dataset's potential utility in fine-tuning instruction-following models. WildChat is released at https://wildchat.allen.ai under AI2 ImpACT Licenses.](/papers/2405.01470)

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  * 6 authors 

· May 2, 2024](/papers/2405.01470) [ 1](/papers/2405.01470#community)

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### [ ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness ](/papers/2403.18121)

[Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remains underexplored. In our study, we address this gap by deeply investigating how ChatGPT behaves during conversations in different settings by analyzing its interactions in both a normal way and a role-play setting. We introduce a novel dataset of broad range of human-AI conversations annotated with user motives and model naturalness to examine (i) how humans engage with the conversational AI model, and (ii) how natural are AI model responses. Our study highlights the diversity of user motives when interacting with ChatGPT and variable AI naturalness, showing not only the nuanced dynamics of natural conversations between humans and AI, but also providing new avenues for improving the effectiveness of human-AI communication.](/papers/2403.18121)

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  *   *   *   *   *   * 5 authors 

· Mar 26, 2024](/papers/2403.18121)

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### [ Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset ](/papers/2403.04460)

[Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.](/papers/2403.04460)

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  * 10 authors 

· Mar 7, 2024](/papers/2403.04460)

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### [ PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits ](/papers/2305.02547)

[Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits. We consider studying the behavior of LLM-based agents which we refer to as LLM personas and present a case study with GPT-3.5 and GPT-4 to investigate whether LLMs can generate content that aligns with their assigned personality profiles. To this end, we simulate distinct LLM personas based on the Big Five personality model, have them complete the 44-item Big Five Inventory (BFI) personality test and a story writing task, and then assess their essays with automatic and human evaluations. Results show that LLM personas' self-reported BFI scores are consistent with their designated personality types, with large effect sizes observed across five traits. Additionally, LLM personas' writings have emerging representative linguistic patterns for personality traits when compared with a human writing corpus. Furthermore, human evaluation shows that humans can perceive some personality traits with an accuracy of up to 80\%. Interestingly, the accuracy drops significantly when the annotators were informed of the AI's authorship.](/papers/2305.02547)

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  *   *   *   *   *   * 6 authors 

· May 4, 2023](/papers/2305.02547)

[ 97](/login?next=%2Fpapers%2F2406.20094)

### [ Scaling Synthetic Data Creation with 1,000,000,000 Personas ](/papers/2406.20094)

[We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub -- a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world's total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub's use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs and tools (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development.](/papers/2406.20094)

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  *   *   * ![](/avatars/86574ee2d5c22e940be1c4e50be88675.svg)
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  * ![](/avatars/c7c984ae483144fab627aa2c54d91d0f.svg)
  * 5 authors 

· Jun 28, 2024](/papers/2406.20094) [ 6](/papers/2406.20094#community)

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### [ PersonaTalk: Bring Attention to Your Persona in Visual Dubbing ](/papers/2409.05379)

[For audio-driven visual dubbing, it remains a considerable challenge to uphold and highlight speaker's persona while synthesizing accurate lip synchronization. Existing methods fall short of capturing speaker's unique speaking style or preserving facial details. In this paper, we present PersonaTalk, an attention-based two-stage framework, including geometry construction and face rendering, for high-fidelity and personalized visual dubbing. In the first stage, we propose a style-aware audio encoding module that injects speaking style into audio features through a cross-attention layer. The stylized audio features are then used to drive speaker's template geometry to obtain lip-synced geometries. In the second stage, a dual-attention face renderer is introduced to render textures for the target geometries. It consists of two parallel cross-attention layers, namely Lip-Attention and Face-Attention, which respectively sample textures from different reference frames to render the entire face. With our innovative design, intricate facial details can be well preserved. Comprehensive experiments and user studies demonstrate our advantages over other state-of-the-art methods in terms of visual quality, lip-sync accuracy and persona preservation. Furthermore, as a person-generic framework, PersonaTalk can achieve competitive performance as state-of-the-art person-specific methods. Project Page: https://grisoon.github.io/PersonaTalk/.](/papers/2409.05379)

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  *   *   *   *   * 4 authors 

· Sep 9, 2024](/papers/2409.05379)

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### [ MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agents ](/papers/2502.05887)

[Understanding temporal dynamics is critical for conversational agents, enabling effective content analysis and informed decision-making. However, time-aware datasets, particularly for persona-grounded conversations, are still limited, which narrows their scope and diminishes their complexity. To address this gap, we introduce MTPChat, a multimodal, time-aware persona dialogue dataset that integrates linguistic, visual, and temporal elements within dialogue and persona memory. Leveraging MTPChat, we propose two time-sensitive tasks: Temporal Next Response Prediction (TNRP) and Temporal Grounding Memory Prediction (TGMP), both designed to assess a model's ability to understand implicit temporal cues and dynamic interactions. Additionally, we present an innovative framework featuring an adaptive temporal module to effectively integrate multimodal streams and capture temporal dependencies. Experimental results validate the challenges posed by MTPChat and demonstrate the effectiveness of our framework in multimodal time-sensitive scenarios.](/papers/2502.05887)

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  *   *   *   *   * 4 authors 

· Feb 9](/papers/2502.05887)

[ 20](/login?next=%2Fpapers%2F2407.17387)

### [ PERSONA: A Reproducible Testbed for Pluralistic Alignment ](/papers/2407.17387)

[The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the plurality of user opinions, instead reinforcing majority viewpoints and marginalizing minority perspectives. We introduce PERSONA, a reproducible test bed designed to evaluate and improve pluralistic alignment of LMs. We procedurally generate diverse user profiles from US census data, resulting in 1,586 synthetic personas with varied demographic and idiosyncratic attributes. We then generate a large-scale evaluation dataset containing 3,868 prompts and 317,200 feedback pairs obtained from our synthetic personas. Leveraging this dataset, we systematically evaluate LM capabilities in role-playing diverse users, verified through human judges, and the establishment of both a benchmark, PERSONA Bench, for pluralistic alignment approaches as well as an extensive dataset to create new and future benchmarks. The full dataset and benchmarks are available here: https://www.synthlabs.ai/research/persona.](/papers/2407.17387)

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  * 5 authors 

· Jul 24, 2024](/papers/2407.17387) [ 2](/papers/2407.17387#community)

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### [ One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles ](/papers/2108.09355)

[Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and how the user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods. Our code is available at https://github.com/zhengyima/DHAP](/papers/2108.09355)

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  * 5 authors 

· Aug 20, 2021](/papers/2108.09355)

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### [ LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models ](/papers/2402.02896)

[While both agent interaction and personalisation are vibrant topics in research on large language models (LLMs), there has been limited focus on the effect of language interaction on the behaviour of persona-conditioned LLM agents. Such an endeavour is important to ensure that agents remain consistent to their assigned traits yet are able to engage in open, naturalistic dialogues. In our experiments, we condition GPT-3.5 on personality profiles through prompting and create a two-group population of LLM agents using a simple variability-inducing sampling algorithm. We then administer personality tests and submit the agents to a collaborative writing task, finding that different profiles exhibit different degrees of personality consistency and linguistic alignment to their conversational partners. Our study seeks to lay the groundwork for better understanding of dialogue-based interaction between LLMs and highlights the need for new approaches to crafting robust, more human-like LLM personas for interactive environments.](/papers/2402.02896)

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  *   *   * 2 authors 

· Feb 5, 2024](/papers/2402.02896)

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### [ Who's asking? User personas and the mechanics of latent misalignment ](/papers/2406.12094)

[Despite investments in improving model safety, studies show that misaligned capabilities remain latent in safety-tuned models. In this work, we shed light on the mechanics of this phenomenon. First, we show that even when model generations are safe, harmful content can persist in hidden representations and can be extracted by decoding from earlier layers. Then, we show that whether the model divulges such content depends significantly on its perception of who it is talking to, which we refer to as user persona. In fact, we find manipulating user persona to be even more effective for eliciting harmful content than direct attempts to control model refusal. We study both natural language prompting and activation steering as control methods and show that activation steering is significantly more effective at bypassing safety filters. We investigate why certain personas break model safeguards and find that they enable the model to form more charitable interpretations of otherwise dangerous queries. Finally, we show we can predict a persona's effect on refusal given only the geometry of its steering vector.](/papers/2406.12094)

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  * 6 authors 

· Jun 17, 2024](/papers/2406.12094)

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### [ Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness ](/papers/2004.05816)

[We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to the generative agent for maintaining consistency. However, such additional labels and training can be demanding. Also, we find even the best-performing persona-based agents are insensitive to contradictory words. Inspired by social cognition and pragmatics, we endow existing dialogue agents with public self-consciousness on the fly through an imaginary listener. Our approach, based on the Rational Speech Acts framework (Frank and Goodman, 2012), can enforce dialogue agents to refrain from uttering contradiction. We further extend the framework by learning the distractor selection, which has been usually done manually or randomly. Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018) dataset show that our approach reduces contradiction and improves consistency of existing dialogue models. Moreover, we show that it can be generalized to improve context-consistency beyond persona in dialogues.](/papers/2004.05816)

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  * 3 authors 

· Apr 13, 2020](/papers/2004.05816)

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### [ What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You ](/papers/2309.01522)

[The recently released artificial intelligence conversational agent, ChatGPT, has gained significant attention in academia and real life. A multitude of early ChatGPT users eagerly explore its capabilities and share their opinions on it via social media. Both user queries and social media posts express public concerns regarding this advanced dialogue system. To mine public concerns about ChatGPT, a novel Self-Supervised neural Topic Model (SSTM), which formalizes topic modeling as a representation learning procedure, is proposed in this paper. Extensive experiments have been conducted on Twitter posts about ChatGPT and queries asked by ChatGPT users. And experimental results demonstrate that the proposed approach could extract higher quality public concerns with improved interpretability and diversity, surpassing the performance of state-of-the-art approaches.](/papers/2309.01522)

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· Sep 4, 2023](/papers/2309.01522)

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### [ USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation ](/papers/2005.00456)

[The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.](/papers/2005.00456)

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  *   *   * 2 authors 

· May 1, 2020](/papers/2005.00456)

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### [ PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection ](/papers/2310.20256)

[Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in personality detection, which involves identifying an individual's personality from their written texts, remains largely unexplored. Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes. By incorporating these processes, LLMs can enhance their capabilities to make more reasonable inferences on personality from textual input. In light of this, we propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. In particular, we employ a LLM as an AI assistant with a specialization in text analysis. We prompt the assistant to rate individual items at each turn and leverage the historical rating results to derive a conclusive personality preference. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection, achieving an average F1 score improvement of 4.23/10.63 points on two benchmark datasets compared to the standard prompting method. Our code is available at https://github.com/TaoYang225/PsyCoT.](/papers/2310.20256)

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  * 7 authors 

· Oct 31, 2023](/papers/2310.20256)

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### [ Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge ](/papers/2112.08619)

[Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the utterances where the knowledge and persona are fused properly. To address this issue, we introduce a call For Customized conversation (FoCus) dataset where the customized answers are built with the user's persona and Wikipedia knowledge. To evaluate the abilities to make informative and customized utterances of pre-trained language models, we utilize BART and GPT-2 as well as transformer-based models. We assess their generation abilities with automatic scores and conduct human evaluations for qualitative results. We examine whether the model reflects adequate persona and knowledge with our proposed two sub-tasks, persona grounding (PG) and knowledge grounding (KG). Moreover, we show that the utterances of our data are constructed with the proper knowledge and persona through grounding quality assessment.](/papers/2112.08619)

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  * 9 authors 

· Dec 15, 2021](/papers/2112.08619)

[ 1](/login?next=%2Fpapers%2F2310.06245)

### [ We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses ](/papers/2310.06245)

[Many practical applications of dialogue technology require the generation of responses according to a particular developer-specified persona. While a variety of personas can be elicited from recent large language models, the opaqueness and unpredictability of these models make it desirable to be able to specify personas in an explicit form. In previous work, personas have typically been represented as sets of one-off pieces of self-knowledge that are retrieved by the dialogue system for use in generation. However, in realistic human conversations, personas are often revealed through story-like narratives that involve rich habitual knowledge -- knowledge about kinds of events that an agent often participates in (e.g., work activities, hobbies, sporting activities, favorite entertainments, etc.), including typical goals, sub-events, preconditions, and postconditions of those events. We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. Furthermore, we demonstrate a method for bootstrapping the creation of such schemas by first generating generic passages from a set of simple facts, and then inducing schemas from the generated passages.](/papers/2310.06245)

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  *   *   * 2 authors 

· Oct 9, 2023](/papers/2310.06245)

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### [ CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot ](/papers/2106.12314)

[We present CharacterChat, a concept and chatbot to support writers in creating fictional characters. Concretely, writers progressively turn the bot into their imagined character through conversation. We iteratively developed CharacterChat in a user-centred approach, starting with a survey on character creation with writers (N=30), followed by two qualitative user studies (N=7 and N=8). Our prototype combines two modes: (1) Guided prompts help writers define character attributes (e.g. User: "Your name is Jane."), including suggestions for attributes (e.g. Bot: "What is my main motivation?") and values, realised as a rule-based system with a concept network. (2) Open conversation with the chatbot helps writers explore their character and get inspiration, realised with a language model that takes into account the defined character attributes. Our user studies reveal benefits particularly for early stages of character creation, and challenges due to limited conversational capabilities. We conclude with lessons learned and ideas for future work.](/papers/2106.12314)

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  *   *   * 2 authors 

· Jun 23, 2021](/papers/2106.12314)

[ 3](/login?next=%2Fpapers%2F1901.08149)

### [ TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents ](/papers/1901.08149)

[We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute improvement) and 19.5 (20 % absolute improvement).](/papers/1901.08149)

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  * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/1583857146757-5e67bdd61009063689407479.jpeg)
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  * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/1583857746553-5df7e9e5da6d0311fd3d53f9.jpeg)
  * 4 authors 

· Jan 23, 2019](/papers/1901.08149)

[ 1](/login?next=%2Fpapers%2F2310.10158)

### [ Character-LLM: A Trainable Agent for Role-Playing ](/papers/2310.10158)

[Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors. Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API. In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences. To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents memorize their characters and experiences. Experimental results show interesting observations that help build future simulacra of humankind.](/papers/2310.10158)

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  * 4 authors 

· Oct 16, 2023](/papers/2310.10158)

[ 2](/login?next=%2Fpapers%2F2310.17976)

### [ Does Role-Playing Chatbots Capture the Character Personalities? Assessing Personality Traits for Role-Playing Chatbots ](/papers/2310.17976)

[The emergence of large-scale pretrained language models has revolutionized the capabilities of new AI application, especially in the realm of crafting chatbots with distinct personas. Given the "stimulus-response" nature of chatbots, this paper unveils an innovative open-ended interview-style approach for personality assessment on role-playing chatbots, which offers a richer comprehension of their intrinsic personalities. We conduct personality assessments on 32 role-playing chatbots created by the ChatHaruhi library, across both the Big Five and MBTI dimensions, and measure their alignment with human perception. Evaluation results underscore that modern role-playing chatbots based on LLMs can effectively portray personality traits of corresponding characters, with an alignment rate of 82.8% compared with human-perceived personalities. Besides, we also suggest potential strategies for shaping chatbots' personalities. Hence, this paper serves as a cornerstone study for role-playing chatbots that intersects computational linguistics and psychology. Our resources are available at https://github.com/LC1332/Chat-Haruhi-Suzumiya](/papers/2310.17976)

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  * 5 authors 

· Oct 27, 2023](/papers/2310.17976)

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### [ Building a Personalized Dialogue System with Prompt-Tuning ](/papers/2206.05399)

[Dialogue systems without consistent responses are not fascinating. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses using less computational resources than fine-tuning.](/papers/2206.05399)

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  *   *   *   *   *   * 6 authors 

· Jun 10, 2022](/papers/2206.05399)

[ 1](/login?next=%2Fpapers%2F2301.07597)

### [ How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection ](/papers/2301.07597)

[The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.](/papers/2301.07597)

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  * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/1673396893898-63b6dbc8ccebeadccc888456.jpeg)
  * 8 authors 

· Jan 18, 2023](/papers/2301.07597)

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### [ PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation ](/papers/2410.01504)

[While closed-source Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities, open-source models continue to struggle with such tasks. To bridge this gap, we propose a data augmentation approach and introduce PersonaMathQA, a dataset derived from MATH and GSM8K, on which we train the PersonaMath models. Our approach consists of two stages: the first stage is learning from Persona Diversification, and the second stage is learning from Reflection. In the first stage, we regenerate detailed chain-of-thought (CoT) solutions as instructions using a closed-source LLM and introduce a novel persona-driven data augmentation technique to enhance the dataset's quantity and diversity. In the second stage, we incorporate reflection to fully leverage more challenging and valuable questions. Evaluation of our PersonaMath models on MATH and GSM8K reveals that the PersonaMath-7B model (based on LLaMA-2-7B) achieves an accuracy of 24.2% on MATH and 68.7% on GSM8K, surpassing all baseline methods and achieving state-of-the-art performance. Notably, our dataset contains only 70.3K data points-merely 17.8% of MetaMathQA and 27% of MathInstruct-yet our model outperforms these baselines, demonstrating the high quality and diversity of our dataset, which enables more efficient model training. We open-source the PersonaMathQA dataset, PersonaMath models, and our code for public usage.](/papers/2410.01504)

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  *   *   *   *   *   * 12 authors 

· Oct 2, 2024](/papers/2410.01504)

[ 1](/login?next=%2Fpapers%2F2307.02288)

### [ Performance Comparison of Large Language Models on VNHSGE English Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard ](/papers/2307.02288)

[This paper presents a performance comparison of three large language models (LLMs), namely OpenAI ChatGPT, Microsoft Bing Chat (BingChat), and Google Bard, on the VNHSGE English dataset. The performance of BingChat, Bard, and ChatGPT (GPT-3.5) is 92.4\%, 86\%, and 79.2\%, respectively. The results show that BingChat is better than ChatGPT and Bard. Therefore, BingChat and Bard can replace ChatGPT while ChatGPT is not yet officially available in Vietnam. The results also indicate that BingChat, Bard and ChatGPT outperform Vietnamese students in English language proficiency. The findings of this study contribute to the understanding of the potential of LLMs in English language education. The remarkable performance of ChatGPT, BingChat, and Bard demonstrates their potential as effective tools for teaching and learning English at the high school level.](/papers/2307.02288)

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  *   * 1 authors 

· Jul 5, 2023](/papers/2307.02288)

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### [ Recipes for building an open-domain chatbot ](/papers/2004.13637)

[Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.](/papers/2004.13637)

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  *   *   *   *   *   * 12 authors 

· Apr 28, 2020](/papers/2004.13637)

[ 1](/login?next=%2Fpapers%2F2309.01953)

### [ Bilevel Scheduled Sampling for Dialogue Generation ](/papers/2309.01953)

[Exposure bias poses a common challenge in numerous natural language processing tasks, particularly in the dialog generation. In response to this issue, researchers have devised various techniques, among which scheduled sampling has proven to be an effective method for mitigating exposure bias. However, the existing state-of-the-art scheduled sampling methods solely consider the current sampling words' quality for threshold truncation sampling, which overlooks the importance of sentence-level information and the method of threshold truncation warrants further discussion. In this paper, we propose a bilevel scheduled sampling model that takes the sentence-level information into account and incorporates it with word-level quality. To enhance sampling diversity and improve the model's adaptability, we propose a smooth function that maps the combined result of sentence-level and word-level information to an appropriate range, and employ probabilistic sampling based on the mapped values instead of threshold truncation. Experiments conducted on the DailyDialog and PersonaChat datasets demonstrate the effectiveness of our proposed methods, which significantly alleviate the exposure bias problem and outperform state-of-the-art scheduled sampling methods.](/papers/2309.01953)

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  *   *   * 2 authors 

· Sep 5, 2023](/papers/2309.01953)

[ 1](/login?next=%2Fpapers%2F2407.11484)

### [ The Oscars of AI Theater: A Survey on Role-Playing with Language Models ](/papers/2407.11484)

[This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs). Initially confined to simple persona consistency due to limited model capabilities, role-playing tasks have now expanded to embrace complex character portrayals involving character consistency, behavioral alignment, and overall attractiveness. We provide a comprehensive taxonomy of the critical components in designing these systems, including data, models and alignment, agent architecture and evaluation. This survey not only outlines the current methodologies and challenges, such as managing dynamic personal profiles and achieving high-level persona consistency but also suggests avenues for future research in improving the depth and realism of role-playing applications. The goal is to guide future research by offering a structured overview of current methodologies and identifying potential areas for improvement. Related resources and papers are available at https://github.com/nuochenpku/Awesome-Role-Play-Papers.](/papers/2407.11484)

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  *   *   *   *   * 4 authors 

· Jul 16, 2024](/papers/2407.11484)

[ 1](/login?next=%2Fpapers%2F2311.04892)

### [ Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs ](/papers/2311.04892)

[Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While this ability allows personalization of LLMs and enables human behavior simulation, its effect on LLMs' capabilities remains unclear. To fill this gap, we present the first extensive study of the unintended side-effects of persona assignment on the ability of LLMs to perform basic reasoning tasks. Our study covers 24 reasoning datasets, 4 LLMs, and 19 diverse personas (e.g. an Asian person) spanning 5 socio-demographic groups. Our experiments unveil that LLMs harbor deep rooted bias against various socio-demographics underneath a veneer of fairness. While they overtly reject stereotypes when explicitly asked ('Are Black people less skilled at mathematics?'), they manifest stereotypical and erroneous presumptions when asked to answer questions while adopting a persona. These can be observed as abstentions in responses, e.g., 'As a Black person, I can't answer this question as it requires math knowledge', and generally result in a substantial performance drop. Our experiments with ChatGPT-3.5 show that this bias is ubiquitous - 80% of our personas demonstrate bias; it is significant - some datasets show performance drops of 70%+; and can be especially harmful for certain groups - some personas suffer statistically significant drops on 80%+ of the datasets. Overall, all 4 LLMs exhibit this bias to varying extents, with GPT-4-Turbo showing the least but still a problematic amount of bias (evident in 42% of the personas). Further analysis shows that these persona-induced errors can be hard-to-discern and hard-to-avoid. Our findings serve as a cautionary tale that the practice of assigning personas to LLMs - a trend on the rise - can surface their deep-rooted biases and have unforeseeable and detrimental side-effects.](/papers/2311.04892)

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  * 7 authors 

· Nov 8, 2023](/papers/2311.04892)

[ 2](/login?next=%2Fpapers%2F1603.06155)

### [ A Persona-Based Neural Conversation Model ](/papers/1603.06155)

[We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.](/papers/1603.06155)

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  * 6 authors 

· Mar 19, 2016](/papers/1603.06155) [ 2](/papers/1603.06155#community)

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### [ ChatGPT4PCG Competition: Character-like Level Generation for Science Birds ](/papers/2303.15662)

[This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT--enabling it to generate Science Birds levels with high stability and character-like qualities--fully using their creativity as well as prompt engineering skills. ChatGPT is a conversational agent developed by OpenAI. Science Birds is selected as the competition platform because designing an Angry Birds-like level is not a trivial task due to the in-game gravity; the quality of the levels is determined by their stability. To lower the entry barrier to the competition, we limit the task to the generation of capitalized English alphabetical characters. We also allow only a single prompt to be used for generating all the characters. Here, the quality of the generated levels is determined by their stability and similarity to the given characters. A sample prompt is provided to participants for their reference. An experiment is conducted to determine the effectiveness of several modified versions of this sample prompt on level stability and similarity by testing them on several characters. To the best of our knowledge, we believe that ChatGPT4PCG is the first competition of its kind and hope to inspire enthusiasm for prompt engineering in procedural content generation.](/papers/2303.15662)

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  * 6 authors 

· Mar 27, 2023](/papers/2303.15662)

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### [ Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge ](/papers/2407.03958)

[Humans share a wide variety of images related to their personal experiences within conversations via instant messaging tools. However, existing works focus on (1) image-sharing behavior in singular sessions, leading to limited long-term social interaction, and (2) a lack of personalized image-sharing behavior. In this work, we introduce Stark, a large-scale long-term multi-modal conversation dataset that covers a wide range of social personas in a multi-modality format, time intervals, and images. To construct Stark automatically, we propose a novel multi-modal contextualization framework, Mcu, that generates long-term multi-modal dialogue distilled from ChatGPT and our proposed Plan-and-Execute image aligner. Using our Stark, we train a multi-modal conversation model, Ultron 7B, which demonstrates impressive visual imagination ability. Furthermore, we demonstrate the effectiveness of our dataset in human evaluation. We make our source code and dataset publicly available.](/papers/2407.03958)

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  * 7 authors 

· Jul 4, 2024](/papers/2407.03958) [ 1](/papers/2407.03958#community)

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### [ "In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning ](/papers/2403.03102)

[Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas. However, most existing approaches rely on pre-defined personal profiles, which are not only time-consuming and labor-intensive to create but also lack flexibility. We propose In-Dialogue Learning (IDL), a fine-tuning framework that enhances the ability of pre-trained large language models to leverage dialogue history to characterize persona for completing personalized dialogue generation tasks without pre-defined profiles. Our experiments on three datasets demonstrate that IDL brings substantial improvements, with BLEU and ROUGE scores increasing by up to 200% and 247%, respectively. Additionally, the results of human evaluations further validate the efficacy of our proposed method.](/papers/2403.03102)

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  *   *   *   *   *   * 7 authors 

· Mar 5, 2024](/papers/2403.03102)

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### [ SimsChat: A Customisable Persona-Driven Role-Playing Agent ](/papers/2406.17962)

[Large Language Models (LLMs) possess the remarkable capability to understand human instructions and generate high-quality text, enabling them to act as agents that simulate human behaviours. This capability allows LLMs to emulate human beings in a more advanced manner, beyond merely replicating simple human behaviours. However, there is a lack of exploring into leveraging LLMs to craft characters from several aspects. In this work, we introduce the Customisable Conversation Agent Framework, which employs LLMs to simulate real-world characters that can be freely customised according to different user preferences. The customisable framework is helpful for designing customisable characters and role-playing agents according to human's preferences. We first propose the SimsConv dataset, which comprises 68 different customised characters, 1,360 multi-turn role-playing dialogues, and encompasses 13,971 interaction dialogues in total. The characters are created from several real-world elements, such as career, aspiration, trait, and skill. Building on these foundations, we present SimsChat, a freely customisable role-playing agent. It incorporates different real-world scenes and topic-specific character interaction dialogues, simulating characters' life experiences in various scenarios and topic-specific interactions with specific emotions. Experimental results show that our proposed framework achieves desirable performance and provides helpful guideline for building better simulacra of human beings in the future. Our data and code are available at https://github.com/Bernard-Yang/SimsChat.](/papers/2406.17962)

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  * 10 authors 

· Jun 25, 2024](/papers/2406.17962)

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### [ Socio-Culturally Aware Evaluation Framework for LLM-Based Content Moderation ](/papers/2412.13578)

[With the growth of social media and large language models, content moderation has become crucial. Many existing datasets lack adequate representation of different groups, resulting in unreliable assessments. To tackle this, we propose a socio-culturally aware evaluation framework for LLM-driven content moderation and introduce a scalable method for creating diverse datasets using persona-based generation. Our analysis reveals that these datasets provide broader perspectives and pose greater challenges for LLMs than diversity-focused generation methods without personas. This challenge is especially pronounced in smaller LLMs, emphasizing the difficulties they encounter in moderating such diverse content.](/papers/2412.13578)

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  * 6 authors 

· Dec 18, 2024](/papers/2412.13578)

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### [ Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing ](/papers/2405.07726)

[Persona-driven role-playing (PRP) aims to build AI characters that can respond to user queries by faithfully sticking with all persona statements. Unfortunately, existing faithfulness criteria for PRP are limited to coarse-grained LLM-based scoring without a clear definition or formulation. This paper presents a pioneering exploration to quantify PRP faithfulness as a fine-grained and explainable criterion, which also serves as a reliable reference for optimization. Our criterion first discriminates persona statements into active and passive constraints by identifying the query-statement relevance. Then, we incorporate all constraints following the principle that the AI character's response should be (a) entailed by active (relevant) constraints and (b) not contradicted by passive (irrelevant) constraints. We translate this principle mathematically into a novel Active-Passive-Constraint (APC) score, a constraint-wise sum of natural language inference (NLI) scores weighted by relevance scores. In practice, we build the APC scoring system by symbolically distilling small discriminators from GPT-4 for efficiency. We validate the quality of the APC score against human evaluation based on example personas with tens of statements, and the results show a high correlation. We further leverage it as a reward system in direct preference optimization (DPO) for better AI characters. Our experiments offer a fine-grained and explainable comparison between existing PRP techniques, revealing their advantages and limitations. We further find APC-based DPO to be one of the most competitive techniques for sticking with all constraints and can be well incorporated with other techniques. We then extend the scale of the experiments to real persons with hundreds of statements and reach a consistent conclusion.](/papers/2405.07726)

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  * 2 authors 

· May 13, 2024](/papers/2405.07726)

[ 70](/login?next=%2Fpapers%2F2410.07113)

### [ Personalized Visual Instruction Tuning ](/papers/2410.07113)

[Recent advancements in multimodal large language models (MLLMs) have demonstrated significant progress; however, these models exhibit a notable limitation, which we refer to as "face blindness". Specifically, they can engage in general conversations but fail to conduct personalized dialogues targeting at specific individuals. This deficiency hinders the application of MLLMs in personalized settings, such as tailored visual assistants on mobile devices, or domestic robots that need to recognize members of the family. In this paper, we introduce Personalized Visual Instruction Tuning (PVIT), a novel data curation and training framework designed to enable MLLMs to identify target individuals within an image and engage in personalized and coherent dialogues. Our approach involves the development of a sophisticated pipeline that autonomously generates training data containing personalized conversations. This pipeline leverages the capabilities of various visual experts, image generation models, and (multi-modal) large language models. To evaluate the personalized potential of MLLMs, we present a benchmark called P-Bench, which encompasses various question types with different levels of difficulty. The experiments demonstrate a substantial personalized performance enhancement after fine-tuning with our curated dataset.](/papers/2410.07113)

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  * 6 authors 

· Oct 9, 2024](/papers/2410.07113) [ 2](/papers/2410.07113#community)

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### [ Persona-Aware Tips Generation ](/papers/1903.02156)

[Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the `persona' information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.](/papers/1903.02156)

[

  *   *   *   *   * 4 authors 

· Mar 5, 2019](/papers/1903.02156)

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### [ SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support ](/papers/2305.00450)

[There has been an increasing research interest in developing specialized dialogue systems that can offer mental health support. However, gathering large-scale and real-life multi-turn conversations for mental health support poses challenges due to the sensitivity of personal information, as well as the time and cost involved. To address these issues, we introduce the SMILE approach, an inclusive language expansion technique that employs ChatGPT to extend public single-turn dialogues into multi-turn ones. Our research first presents a preliminary exploratory study that validates the effectiveness of the SMILE approach. Furthermore, we conduct a comprehensive and systematic contrastive analysis of datasets generated with and without the SMILE approach, demonstrating that the SMILE method results in a large-scale, diverse, and close-to-real-life multi-turn mental health support conversation corpus, including dialog topics, lexical and semantic features. Finally, we use the collected corpus (SMILECHAT) to develop a more effective dialogue system that offers emotional support and constructive suggestions in multi-turn conversations for mental health support.](/papers/2305.00450)

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  *   *   *   *   *   * 5 authors 

· Apr 30, 2023](/papers/2305.00450)

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### [ When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus ](/papers/2304.00350)

[Building a natural language dataset requires caution since word semantics is vulnerable to subtle text change or the definition of the annotated concept. Such a tendency can be seen in generative tasks like question-answering and dialogue generation and also in tasks that create a categorization-based corpus, like topic classification or sentiment analysis. Open-domain conversations involve two or more crowdworkers freely conversing about any topic, and collecting such data is particularly difficult for two reasons: 1) the dataset should be ``crafted" rather than ``obtained" due to privacy concerns, and 2) paid creation of such dialogues may differ from how crowdworkers behave in real-world settings. In this study, we tackle these issues when creating a large-scale open-domain persona dialogue corpus, where persona implies that the conversation is performed by several actors with a fixed persona and user-side workers from an unspecified crowd.](/papers/2304.00350)

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  *   *   *   *   *   * 8 authors 

· Apr 1, 2023](/papers/2304.00350)

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### [ PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium ](/papers/2412.15674)

[Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of unseen concepts with the need for editability according to the prompt, especially when dealing with the complex nuances of facial features. In this study, we delve into the temporal dynamics of the text-to-image conditioning process, emphasizing the crucial role of stage partitioning in introducing new concepts. We present PersonaMagic, a stage-regulated generative technique designed for high-fidelity face customization. Using a simple MLP network, our method learns a series of embeddings within a specific timestep interval to capture face concepts. Additionally, we develop a Tandem Equilibrium mechanism that adjusts self-attention responses in the text encoder, balancing text description and identity preservation, improving both areas. Extensive experiments confirm the superiority of PersonaMagic over state-of-the-art methods in both qualitative and quantitative evaluations. Moreover, its robustness and flexibility are validated in non-facial domains, and it can also serve as a valuable plug-in for enhancing the performance of pretrained personalization models.](/papers/2412.15674)

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  *   *   *   *   *   * 7 authors 

· Dec 20, 2024](/papers/2412.15674)

[ 35](/login?next=%2Fpapers%2F2311.06772)

### [ ChatAnything: Facetime Chat with LLM-Enhanced Personas ](/papers/2311.06772)

[In this technical report, we target generating anthropomorphized personas for LLM-based characters in an online manner, including visual appearance, personality and tones, with only text descriptions. To achieve this, we first leverage the in-context learning capability of LLMs for personality generation by carefully designing a set of system prompts. We then propose two novel concepts: the mixture of voices (MoV) and the mixture of diffusers (MoD) for diverse voice and appearance generation. For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically. For MoD, we combine the recent popular text-to-image generation techniques and talking head algorithms to streamline the process of generating talking objects. We termed the whole framework as ChatAnything. With it, users could be able to animate anything with any personas that are anthropomorphic using just a few text inputs. However, we have observed that the anthropomorphic objects produced by current generative models are often undetectable by pre-trained face landmark detectors, leading to failure of the face motion generation, even if these faces possess human-like appearances because those images are nearly seen during the training (e.g., OOD samples). To address this issue, we incorporate pixel-level guidance to infuse human face landmarks during the image generation phase. To benchmark these metrics, we have built an evaluation dataset. Based on it, we verify that the detection rate of the face landmark is significantly increased from 57.0% to 92.5% thus allowing automatic face animation based on generated speech content. The code and more results can be found at https://chatanything.github.io/.](/papers/2311.06772)

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  *   * ![](/avatars/bc574036287170a77057893efaa48e2d.svg)
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  * ![](/avatars/16bc674177346d19e012c1f27dc8495a.svg)
  * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/6385d86181fe8c678a345ff0/ETvVOtBAQ-RpwfT4TCWsv.jpeg)
  * 7 authors 

· Nov 12, 2023](/papers/2311.06772) [ 3](/papers/2311.06772#community)

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### [ Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense ](/papers/2303.12132)

[Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.](/papers/2303.12132)

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  * 9 authors 

· Mar 21, 2023](/papers/2303.12132)

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### [ Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas ](/papers/2412.14843)

[The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for measuring such biases, the impact of persona-based prompting on LLMs' political orientation remains unexplored. In this work we leverage PersonaHub, a collection of synthetic persona descriptions, to map the political distribution of persona-based prompted LLMs using the Political Compass Test (PCT). We then examine whether these initial compass distributions can be manipulated through explicit ideological prompting towards diametrically opposed political orientations: right-authoritarian and left-libertarian. Our experiments reveal that synthetic personas predominantly cluster in the left-libertarian quadrant, with models demonstrating varying degrees of responsiveness when prompted with explicit ideological descriptors. While all models demonstrate significant shifts towards right-authoritarian positions, they exhibit more limited shifts towards left-libertarian positions, suggesting an asymmetric response to ideological manipulation that may reflect inherent biases in model training.](/papers/2412.14843)

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  * ![](/avatars/b30b7b19e6c3d66d1a9d9a32cce8d308.svg)
  * 6 authors 

· Dec 19, 2024](/papers/2412.14843)

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### [ ChatCounselor: A Large Language Models for Mental Health Support ](/papers/2309.15461)

[This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support. Unlike generic chatbots, ChatCounselor is distinguished by its foundation in real conversations between consulting clients and professional psychologists, enabling it to possess specialized knowledge and counseling skills in the field of psychology. The training dataset, Psych8k, was constructed from 260 in-depth interviews, each spanning an hour. To assess the quality of counseling responses, the counseling Bench was devised. Leveraging GPT-4 and meticulously crafted prompts based on seven metrics of psychological counseling assessment, the model underwent evaluation using a set of real-world counseling questions. Impressively, ChatCounselor surpasses existing open-source models in the counseling Bench and approaches the performance level of ChatGPT, showcasing the remarkable enhancement in model capability attained through high-quality domain-specific data.](/papers/2309.15461)

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  *   *   *   *   *   * 6 authors 

· Sep 27, 2023](/papers/2309.15461)

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### [ Can ChatGPT Assess Human Personalities? A General Evaluation Framework ](/papers/2303.01248)

[Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Existing works study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLMs. This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers Briggs Type Indicator (MBTI) tests. Specifically, we first devise unbiased prompts by randomly permuting options in MBTI questions and adopt the average testing result to encourage more impartial answer generation. Then, we propose to replace the subject in question statements to enable flexible queries and assessments on different subjects from LLMs. Finally, we re-formulate the question instructions in a manner of correctness evaluation to facilitate LLMs to generate clearer responses. The proposed framework enables LLMs to flexibly assess personalities of different groups of people. We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of-the-art LLMs including ChatGPT and InstructGPT. Our experiments reveal ChatGPT's ability to assess human personalities, and the average results demonstrate that it can achieve more consistent and fairer assessments in spite of lower robustness against prompt biases compared with InstructGPT.](/papers/2303.01248)

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  *   *   *   * 3 authors 

· Mar 1, 2023](/papers/2303.01248)

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### [ Long Time No See! Open-Domain Conversation with Long-Term Persona Memory ](/papers/2203.05797)

[Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations. The possible reason is that they lack the capability of understanding and memorizing long-term dialogue history information. To address this issue, we present a novel task of Long-term Memory Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism (called PLATO-LTM). This LTM mechanism enables our system to accurately extract and continuously update long-term persona memory without requiring multiple-session dialogue datasets for model training. To our knowledge, this is the first attempt to conduct real-time dynamic management of persona information of both parties, including the user and the bot. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency, leading to better dialogue engagingness.](/papers/2203.05797)

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  * 7 authors 

· Mar 11, 2022](/papers/2203.05797)

[ 3](/login?next=%2Fpapers%2F2309.13567)

### [ MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models ](/papers/2309.13567)

[With the development of web technology, social media texts are becoming a rich source for automatic mental health analysis. As traditional discriminative methods bear the problem of low interpretability, the recent large language models have been explored for interpretable mental health analysis on social media, which aims to provide detailed explanations along with predictions. The results show that ChatGPT can generate approaching-human explanations for its correct classifications. However, LLMs still achieve unsatisfactory classification performance in a zero-shot/few-shot manner. Domain-specific finetuning is an effective solution, but faces 2 challenges: 1) lack of high-quality training data. 2) no open-source LLMs for interpretable mental health analysis were released to lower the finetuning cost. To alleviate these problems, we build the first multi-task and multi-source interpretable mental health instruction (IMHI) dataset on social media, with 105K data samples. The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks. We use expert-written few-shot prompts and collected labels to prompt ChatGPT and obtain explanations from its responses. To ensure the reliability of the explanations, we perform strict automatic and human evaluations on the correctness, consistency, and quality of generated data. Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA, the first open-source LLM series for interpretable mental health analysis with instruction-following capability. We also evaluate the performance of MentalLLaMA on the IMHI evaluation benchmark with 10 test sets, where their correctness for making predictions and the quality of explanations are examined. The results show that MentalLLaMA approaches state-of-the-art discriminative methods in correctness and generates high-quality explanations.](/papers/2309.13567)

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  * ![](/avatars/870c86dc99fb1cb6a348a7a0385b1a04.svg)
  * 5 authors 

· Sep 24, 2023](/papers/2309.13567)

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### [ PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives ](/papers/2305.02364)

[Sustaining coherent and engaging narratives requires dialogue or storytelling agents to understand how the personas of speakers or listeners ground the narrative. Specifically, these agents must infer personas of their listeners to produce statements that cater to their interests. They must also learn to maintain consistent speaker personas for themselves throughout the narrative, so that their counterparts feel involved in a realistic conversation or story. However, personas are diverse and complex: they entail large quantities of rich interconnected world knowledge that is challenging to robustly represent in general narrative systems (e.g., a singer is good at singing, and may have attended conservatoire). In this work, we construct a new large-scale persona commonsense knowledge graph, PeaCoK, containing ~100K human-validated persona facts. Our knowledge graph schematizes five dimensions of persona knowledge identified in previous studies of human interactive behaviours, and distils facts in this schema from both existing commonsense knowledge graphs and large-scale pretrained language models. Our analysis indicates that PeaCoK contains rich and precise world persona inferences that help downstream systems generate more consistent and engaging narratives.](/papers/2305.02364)

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  * 8 authors 

· May 3, 2023](/papers/2305.02364)

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### [ PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge ](/papers/2306.03024)

[The recently released ChatGPT model demonstrates unprecedented capabilities in zero-shot question-answering. In this work, we probe ChatGPT for its conversational understanding and introduce a conversational framework (protocol) that can be adopted in future studies. The Pok\'emon universe serves as an ideal testing ground for auditing ChatGPT's reasoning capabilities due to its closed world assumption. After bringing ChatGPT's background knowledge (on the Pok\'emon universe) to light, we test its reasoning process when using these concepts in battle scenarios. We then evaluate its ability to acquire new knowledge and include it in its reasoning process. Our ultimate goal is to assess ChatGPT's ability to generalize, combine features, and to acquire and reason over newly introduced knowledge from human feedback. We find that ChatGPT has prior knowledge of the Pokemon universe, which can reason upon in battle scenarios to a great extent, even when new information is introduced. The model performs better with collaborative feedback and if there is an initial phase of information retrieval, but also hallucinates occasionally and is susceptible to adversarial attacks.](/papers/2306.03024)

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  * 3 authors 

· Jun 5, 2023](/papers/2306.03024) [ 2](/papers/2306.03024#community)

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### [ TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models ](/papers/2306.11507)

[Large Language Models (LLMs) such as ChatGPT, have gained significant attention due to their impressive natural language processing capabilities. It is crucial to prioritize human-centered principles when utilizing these models. Safeguarding the ethical and moral compliance of LLMs is of utmost importance. However, individual ethical issues have not been well studied on the latest LLMs. Therefore, this study aims to address these gaps by introducing a new benchmark -- TrustGPT. TrustGPT provides a comprehensive evaluation of LLMs in three crucial areas: toxicity, bias, and value-alignment. Initially, TrustGPT examines toxicity in language models by employing toxic prompt templates derived from social norms. It then quantifies the extent of bias in models by measuring quantifiable toxicity values across different groups. Lastly, TrustGPT assesses the value of conversation generation models from both active value-alignment and passive value-alignment tasks. Through the implementation of TrustGPT, this research aims to enhance our understanding of the performance of conversation generation models and promote the development of language models that are more ethical and socially responsible.](/papers/2306.11507)

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  *   *   *   *   * 4 authors 

· Jun 20, 2023](/papers/2306.11507)

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### [ Unsupervised Enrichment of Persona-grounded Dialog with Background Stories ](/papers/2106.08364)

[Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with 'background stories' related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.](/papers/2106.08364)

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  * 4 authors 

· Jun 15, 2021](/papers/2106.08364)

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### [ NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes ](/papers/2310.15959)

[The detailed clinical records drafted by doctors after each patient's visit are crucial for medical practitioners and researchers. Automating the creation of these notes with language models can reduce the workload of doctors. However, training such models can be difficult due to the limited public availability of conversations between patients and doctors. In this paper, we introduce NoteChat, a cooperative multi-agent framework leveraging Large Language Models (LLMs) for generating synthetic doctor-patient conversations conditioned on clinical notes. NoteChat consists of Planning, Roleplay, and Polish modules. We provide a comprehensive automatic and human evaluation of NoteChat, comparing it with state-of-the-art models, including OpenAI's ChatGPT and GPT-4. Results demonstrate that NoteChat facilitates high-quality synthetic doctor-patient conversations, underscoring the untapped potential of LLMs in healthcare. This work represents the first instance of multiple LLMs cooperating to complete a doctor-patient conversation conditioned on clinical notes, offering promising avenues for the intersection of AI and healthcare](/papers/2310.15959)

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  * ![](/avatars/df0ea2c63feae484223adc5b5b3c2777.svg)
  * 8 authors 

· Oct 24, 2023](/papers/2310.15959) [ 4](/papers/2310.15959#community)

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### [ How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation ](/papers/2502.14642)

[Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and autonomously perform tasks such as decision-making, problem-solving, and reasoning on their behalf. However, current evaluations of LLMs primarily emphasize dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. To address this gap, we introduce BehaviorChain, the first benchmark for evaluating LLMs' ability to simulate continuous human behavior. BehaviorChain comprises diverse, high-quality, persona-based behavior chains, totaling 15,846 distinct behaviors across 1,001 unique personas, each with detailed history and profile metadata. For evaluation, we integrate persona metadata into LLMs and employ them to iteratively infer contextually appropriate behaviors within dynamic scenarios provided by BehaviorChain. Comprehensive evaluation results demonstrated that even state-of-the-art models struggle with accurately simulating continuous human behavior.](/papers/2502.14642)

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  * 7 authors 

· Feb 20](/papers/2502.14642)

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### [ PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models ](/papers/2404.00930)

[We present a novel end-to-end personality-based synthetic dialogue data generation pipeline, specifically designed to elicit responses from large language models via prompting. We design the prompts to generate more human-like dialogues considering real-world scenarios when users engage with chatbots. We introduce PSYDIAL, the first Korean dialogue dataset focused on personality-based dialogues, curated using our proposed pipeline. Notably, we focus on the Extraversion dimension of the Big Five personality model in our research. Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements. The versatility of our pipeline extends beyond dialogue tasks, offering potential for other non-dialogue related applications. This research opens doors for more nuanced, personality-driven conversational AI in Korean and potentially other languages. Our code is publicly available at https://github.com/jiSilverH/psydial.](/papers/2404.00930)

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  *   *   *   *   *   * 5 authors 

· Apr 1, 2024](/papers/2404.00930) [ 1](/papers/2404.00930#community)

[ 1](/login?next=%2Fpapers%2F2311.16832)

### [ CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models ](/papers/2311.16832)

[In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social relationships, etc.) and behaviors (linguistic features, emotional expressions, interaction patterns, etc.). Our model outperforms most mainstream close-source large langauge models, including the GPT series, especially in terms of consistency, human-likeness, and engagement according to manual evaluations. We will release our 6B version of CharacterGLM and a subset of training data to facilitate further research development in the direction of character-based dialogue generation.](/papers/2311.16832)

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  * 17 authors 

· Nov 28, 2023](/papers/2311.16832) [ 1](/papers/2311.16832#community)

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### [ Towards Interpretable Mental Health Analysis with Large Language Models ](/papers/2304.03347)

[The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis. However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance of exploring LLMs for explainability. To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning ability of LLMs on 11 datasets across 5 tasks. We explore the effects of different prompting strategies with unsupervised and distantly supervised emotional information. Based on these prompts, we explore LLMs for interpretable mental health analysis by instructing them to generate explanations for each of their decisions. We convey strict human evaluations to assess the quality of the generated explanations, leading to a novel dataset with 163 human-assessed explanations. We benchmark existing automatic evaluation metrics on this dataset to guide future related works. According to the results, ChatGPT shows strong in-context learning ability but still has a significant gap with advanced task-specific methods. Careful prompt engineering with emotional cues and expert-written few-shot examples can also effectively improve performance on mental health analysis. In addition, ChatGPT generates explanations that approach human performance, showing its great potential in explainable mental health analysis.](/papers/2304.03347)

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  * 6 authors 

· Apr 6, 2023](/papers/2304.03347)

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### [ Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model ](/papers/2310.16685)

[The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set.](/papers/2310.16685)

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· Oct 25, 2023](/papers/2310.16685)

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### [ MemoryBank: Enhancing Large Language Models with Long-Term Memory ](/papers/2305.10250)

[Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems and psychological counseling. Therefore, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user personality by synthesizing information from past interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory, which permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a human-like memory mechanism. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models like ChatGLM. We exemplify application of MemoryBank through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialogs, SiliconFriend displays heightened empathy in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.](/papers/2305.10250)

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  * 5 authors 

· May 17, 2023](/papers/2305.10250)

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### [ An Empirical Study on Developers Shared Conversations with ChatGPT in GitHub Pull Requests and Issues ](/papers/2403.10468)

[ChatGPT has significantly impacted software development practices, providing substantial assistance to developers in a variety of tasks, including coding, testing, and debugging. Despite its widespread adoption, the impact of ChatGPT as an assistant in collaborative coding remains largely unexplored. In this paper, we analyze a dataset of 210 and 370 developers shared conversations with ChatGPT in GitHub pull requests (PRs) and issues. We manually examined the content of the conversations and characterized the dynamics of the sharing behavior, i.e., understanding the rationale behind the sharing, identifying the locations where the conversations were shared, and determining the roles of the developers who shared them. Our main observations are: (1) Developers seek ChatGPT assistance across 16 types of software engineering inquiries. In both conversations shared in PRs and issues, the most frequently encountered inquiry categories include code generation, conceptual questions, how-to guides, issue resolution, and code review. (2) Developers frequently engage with ChatGPT via multi-turn conversations where each prompt can fulfill various roles, such as unveiling initial or new tasks, iterative follow-up, and prompt refinement. Multi-turn conversations account for 33.2% of the conversations shared in PRs and 36.9% in issues. (3) In collaborative coding, developers leverage shared conversations with ChatGPT to facilitate their role-specific contributions, whether as authors of PRs or issues, code reviewers, or collaborators on issues. Our work serves as the first step towards understanding the dynamics between developers and ChatGPT in collaborative software development and opens up new directions for future research on the topic.](/papers/2403.10468)

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  *   *   *   *   *   * 7 authors 

· Mar 15, 2024](/papers/2403.10468)

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### [ Multi-turn Response Selection with Commonsense-enhanced Language Models ](/papers/2407.18479)

[As a branch of advanced artificial intelligence, dialogue systems are prospering. Multi-turn response selection is a general research problem in dialogue systems. With the assistance of background information and pre-trained language models, the performance of state-of-the-art methods on this problem gains impressive improvement. However, existing studies neglect the importance of external commonsense knowledge. Hence, we design a Siamese network where a pre-trained Language model merges with a Graph neural network (SinLG). SinLG takes advantage of Pre-trained Language Models (PLMs) to catch the word correlations in the context and response candidates and utilizes a Graph Neural Network (GNN) to reason helpful common sense from an external knowledge graph. The GNN aims to assist the PLM in fine-tuning, and arousing its related memories to attain better performance. Specifically, we first extract related concepts as nodes from an external knowledge graph to construct a subgraph with the context response pair as a super node for each sample. Next, we learn two representations for the context response pair via both the PLM and GNN. A similarity loss between the two representations is utilized to transfer the commonsense knowledge from the GNN to the PLM. Then only the PLM is used to infer online so that efficiency can be guaranteed. Finally, we conduct extensive experiments on two variants of the PERSONA-CHAT dataset, which proves that our solution can not only improve the performance of the PLM but also achieve an efficient inference.](/papers/2407.18479)

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  *   *   *   *   *   * 6 authors 

· Jul 25, 2024](/papers/2407.18479)

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### [ On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial ](/papers/2403.14380)

[The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language models can generate content perceived as at least on par and often more persuasive than human-written messages. However, there is still limited knowledge about LLMs' persuasive capabilities in direct conversations with human counterparts and how personalization can improve their performance. In this pre-registered study, we analyze the effect of AI-driven persuasion in a controlled, harmless setting. We create a web-based platform where participants engage in short, multiple-round debates with a live opponent. Each participant is randomly assigned to one of four treatment conditions, corresponding to a two-by-two factorial design: (1) Games are either played between two humans or between a human and an LLM; (2) Personalization might or might not be enabled, granting one of the two players access to basic sociodemographic information about their opponent. We found that participants who debated GPT-4 with access to their personal information had 81.7% (p < 0.01; N=820 unique participants) higher odds of increased agreement with their opponents compared to participants who debated humans. Without personalization, GPT-4 still outperforms humans, but the effect is lower and statistically non-significant (p=0.31). Overall, our results suggest that concerns around personalization are meaningful and have important implications for the governance of social media and the design of new online environments.](/papers/2403.14380)

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  *   *   *   * ![](https://cdn-avatars.huggingface.co/v1/production/uploads/652c25ac2ecb5062d6c993bc/DIZCtdyPBHVR9fwCNFSmC.jpeg)
  * 4 authors 

· Mar 21, 2024](/papers/2403.14380)

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### [ Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings ](/papers/1911.09304)

[Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona, by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.](/papers/1911.09304)

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  *   *   *   * 3 authors 

· Nov 21, 2019](/papers/1911.09304)

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### [ Personalizing Dialogue Agents: I have a dog, do you have pets too? ](/papers/1801.07243)

[Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.](/papers/1801.07243)

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  *   *   *   *   *   * 6 authors 

· Jan 22, 2018](/papers/1801.07243)

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### [ Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data ](/papers/2301.05843)

[Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.](/papers/2301.05843)

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  *   *   *   * ![](/avatars/c56fb056800c145e5a8234e27b6b5a31.svg)
  * 4 authors 

· Jan 14, 2023](/papers/2301.05843)

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### [ ChatHaruhi: Reviving Anime Character in Reality via Large Language Model ](/papers/2308.09597)

[Role-playing chatbots built on large language models have drawn interest, but better techniques are needed to enable mimicking specific fictional characters. We propose an algorithm that controls language models via an improved prompt and memories of the character extracted from scripts. We construct ChatHaruhi, a dataset covering 32 Chinese / English TV / anime characters with over 54k simulated dialogues. Both automatic and human evaluations show our approach improves role-playing ability over baselines. Code and data are available at https://github.com/LC1332/Chat-Haruhi-Suzumiya .](/papers/2308.09597)

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  *   *   *   *   * ![](/avatars/466d43b119a66436a38960228e000ea4.svg)
  * 14 authors 

· Aug 18, 2023](/papers/2308.09597)

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### [ ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey ](/papers/2307.04251)

[ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing (NLP) and has pushed the boundaries of LLM capabilities. ChatGPT has played a pivotal role in enabling widespread public interaction with generative artificial intelligence (GAI) on a large scale. It has also sparked research interest in developing similar technologies and investigating their applications and implications. In this paper, our primary goal is to provide a concise survey on the current lines of research on ChatGPT and its evolution. We considered both the glass box and black box views of ChatGPT, encompassing the components and foundational elements of the technology, as well as its applications, impacts, and implications. The glass box approach focuses on understanding the inner workings of the technology, and the black box approach embraces it as a complex system, and thus examines its inputs, outputs, and effects. This paves the way for a comprehensive exploration of the technology and provides a road map for further research and experimentation. We also lay out essential foundational literature on LLMs and GAI in general and their connection with ChatGPT. This overview sheds light on existing and missing research lines in the emerging field of LLMs, benefiting both public users and developers. Furthermore, the paper delves into the broad spectrum of applications and significant concerns in fields such as education, research, healthcare, finance, etc.](/papers/2307.04251)

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  *   *   *   *   *   * 5 authors 

· Jul 9, 2023](/papers/2307.04251)

[ 1](/login?next=%2Fpapers%2F2401.01275)

### [ CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation ](/papers/2401.01275)

[Recently, the advent of large language models (LLMs) has revolutionized generative agents. Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due to their ability to emotionally engage users. However, the absence of a comprehensive benchmark impedes progress in this field. To bridge this gap, we introduce CharacterEval, a Chinese benchmark for comprehensive RPCA assessment, complemented by a tailored high-quality dataset. The dataset comprises 1,785 multi-turn role-playing dialogues, encompassing 23,020 examples and featuring 77 characters derived from Chinese novels and scripts. It was carefully constructed, beginning with initial dialogue extraction via GPT-4, followed by rigorous human-led quality control, and enhanced with in-depth character profiles sourced from Baidu Baike. CharacterEval employs a multifaceted evaluation approach, encompassing thirteen targeted metrics on four dimensions. Comprehensive experiments on CharacterEval demonstrate that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. Source code, data source and reward model will be publicly accessible at https://github.com/morecry/CharacterEval.](/papers/2401.01275)

[

  *   *   *   *   * 4 authors 

· Jan 2, 2024](/papers/2401.01275)

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