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url: http://arxiv.org/abs/2309.13401v1
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[2309.13401v1] Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

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# Computer Science > Computer Vision and Pattern Recognition

**arXiv:2309.13401v1** (cs) 

[Submitted on 23 Sep 2023]

# Title:Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

Authors:[Hongqiu Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+H), [Jian Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+J), [Shichen Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+S), [Yuan He](https://arxiv.org/search/cs?searchtype=author&query=He,+Y), [Jinfeng Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+J), [Mengwan Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+M), [Jinlan He](https://arxiv.org/search/cs?searchtype=author&query=He,+J), [Wenjun Liao](https://arxiv.org/search/cs?searchtype=author&query=Liao,+W), [Xiangde Luo](https://arxiv.org/search/cs?searchtype=author&query=Luo,+X)

View a PDF of the paper titled Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals, by Hongqiu Wang and 8 other authors

[View PDF](/pdf/2309.13401v1)

> Abstract:Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the UDA methods and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research. 

Subjects: |  Computer Vision and Pattern Recognition (cs.CV)  
---|---  
Cite as: | [arXiv:2309.13401](https://arxiv.org/abs/2309.13401) [cs.CV]  
  | (or  [arXiv:2309.13401v1](https://arxiv.org/abs/2309.13401v1) [cs.CV] for this version)   
  |  <https://doi.org/10.48550/arXiv.2309.13401> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Hongqiu Wang [[view email](/show-email/d222583f/2309.13401)]   
**[v1]** Sat, 23 Sep 2023 15:26:27 UTC (10,478 KB)  


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url: http://arxiv.org/abs/2207.13398v2
title: [2207.13398v2] Emergent social NPC interactions in the Social NPCs Skyrim mod and beyond
scrape_date: 2025-03-11T15:37:03.215885
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# Computer Science > Artificial Intelligence

**arXiv:2207.13398v2** (cs) 

[Submitted on 27 Jul 2022 ([v1](https://arxiv.org/abs/2207.13398v1)), last revised 20 Jan 2023 (this version, v2)]

# Title:Emergent social NPC interactions in the Social NPCs Skyrim mod and beyond

Authors:[Manuel Guimarães](https://arxiv.org/search/cs?searchtype=author&query=Guimar%C3%A3es,+M), [Pedro A. Santos](https://arxiv.org/search/cs?searchtype=author&query=Santos,+P+A), [Arnav Jhala](https://arxiv.org/search/cs?searchtype=author&query=Jhala,+A)

View a PDF of the paper titled Emergent social NPC interactions in the Social NPCs Skyrim mod and beyond, by Manuel Guimar\~aes and 2 other authors

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> Abstract:This work presents an implementation of a social architecture model for authoring Non-Player Character (NPC) in open world games inspired in academic research on agentbased modeling. Believable NPC authoring is burdensome in terms of rich dialogue and responsive behaviors.   
> We briefly present the characteristics and advantages of using a social agent architecture for this task and describe an implementation of a social agent architecture CiF-CK released as a mod Social NPCs for The Elder Scrolls V: Skyrim 

Comments: | Originally a chapter for Game AI Pro, contains 14 pages, 3 figures  
---|---  
Subjects: |  Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)  
Cite as: | [arXiv:2207.13398](https://arxiv.org/abs/2207.13398) [cs.AI]  
  | (or  [arXiv:2207.13398v2](https://arxiv.org/abs/2207.13398v2) [cs.AI] for this version)   
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## Submission history

From: Manuel Guimarães [[view email](/show-email/1d4f6b90/2207.13398)]   
**[[v1]](/abs/2207.13398v1)** Wed, 27 Jul 2022 09:30:23 UTC (639 KB)  
**[v2]** Fri, 20 Jan 2023 16:15:12 UTC (639 KB)  


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url: https://arxiv.org/abs/2407.11484
title: [2407.11484] The Oscars of AI Theater: A Survey on Role-Playing with Language Models
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# Computer Science > Artificial Intelligence

**arXiv:2407.11484** (cs) 

[Submitted on 16 Jul 2024 ([v1](https://arxiv.org/abs/2407.11484v1)), last revised 10 Jan 2025 (this version, v9)]

# Title:The Oscars of AI Theater: A Survey on Role-Playing with Language Models

Authors:[Nuo Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+N), [Yan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y), [Yang Deng](https://arxiv.org/search/cs?searchtype=author&query=Deng,+Y), [Jia Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+J)

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> Abstract: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 [this https URL](https://github.com/nuochenpku/Awesome-Role-Play-Papers). 

Comments: | 28 pages  
---|---  
Subjects: |  Artificial Intelligence (cs.AI); Computation and Language (cs.CL)  
Cite as: | [arXiv:2407.11484](https://arxiv.org/abs/2407.11484) [cs.AI]  
  | (or  [arXiv:2407.11484v9](https://arxiv.org/abs/2407.11484v9) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2407.11484> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Nuo Chen [[view email](/show-email/13ac644f/2407.11484)]   
**[[v1]](/abs/2407.11484v1)** Tue, 16 Jul 2024 08:20:39 UTC (818 KB)  
**[[v2]](/abs/2407.11484v2)** Wed, 17 Jul 2024 09:45:49 UTC (819 KB)  
**[[v3]](/abs/2407.11484v3)** Sun, 21 Jul 2024 15:02:49 UTC (953 KB)  
**[[v4]](/abs/2407.11484v4)** Tue, 23 Jul 2024 13:18:31 UTC (955 KB)  
**[[v5]](/abs/2407.11484v5)** Thu, 22 Aug 2024 15:44:27 UTC (955 KB)  
**[[v6]](/abs/2407.11484v6)** Sun, 1 Sep 2024 10:12:45 UTC (955 KB)  
**[[v7]](/abs/2407.11484v7)** Wed, 30 Oct 2024 07:33:59 UTC (955 KB)  
**[[v8]](/abs/2407.11484v8)** Thu, 7 Nov 2024 03:23:16 UTC (955 KB)  
**[v9]** Fri, 10 Jan 2025 02:18:01 UTC (956 KB)  


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url: http://arxiv.org/abs/1609.04879v1
title: [1609.04879v1] NPCs as People, Too: The Extreme AI Personality Engine
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# Computer Science > Artificial Intelligence

**arXiv:1609.04879v1** (cs) 

[Submitted on 15 Sep 2016]

# Title:NPCs as People, Too: The Extreme AI Personality Engine

Authors:[Jeffrey Georgeson](https://arxiv.org/search/cs?searchtype=author&query=Georgeson,+J), [Christopher Child](https://arxiv.org/search/cs?searchtype=author&query=Child,+C)

View a PDF of the paper titled NPCs as People, Too: The Extreme AI Personality Engine, by Jeffrey Georgeson and Christopher Child

[View PDF](/pdf/1609.04879v1)

> Abstract:PK Dick once asked "Do Androids Dream of Electric Sheep?" In video games, a similar question could be asked of non-player characters: Do NPCs have dreams? Can they live and change as humans do? Can NPCs have personalities, and can these develop through interactions with players, other NPCs, and the world around them? Despite advances in personality AI for games, most NPCs are still undeveloped and undeveloping, reacting with flat affect and predictable routines that make them far less than human--in fact, they become little more than bits of the scenery that give out parcels of information. This need not be the case. Extreme AI, a psychology-based personality engine, creates adaptive NPC personalities. Originally developed as part of the thesis "NPCs as People: Using Databases and Behaviour Trees to Give Non-Player Characters Personality," Extreme AI is now a fully functioning personality engine using all thirty facets of the Five Factor model of personality and an AI system that is live throughout gameplay. This paper discusses the research leading to Extreme AI; develops the ideas found in that thesis; discusses the development of other personality engines; and provides examples of Extreme AI's use in two game demos. 

Comments: | 9 pages, 3 tables, 3 figures  
---|---  
Subjects: |  Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:1609.04879](https://arxiv.org/abs/1609.04879) [cs.AI]  
  | (or  [arXiv:1609.04879v1](https://arxiv.org/abs/1609.04879v1) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.1609.04879> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Jeffrey Georgeson [[view email](/show-email/6f372376/1609.04879)]   
**[v1]** Thu, 15 Sep 2016 22:40:29 UTC (333 KB)  


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url: https://arxiv.org/html/2407.11484v4
title: The Oscars of AI Theater: A Survey on Role-Playing with Language Models
scrape_date: 2025-03-11T15:37:03.508944
content_type: markdown
hash: 8cea330ede7e2f78a413a236918732ac4046bd841bfbb4d67f0b3f2c3de46e0d
content:
  1. [1 Introduction](https://arxiv.org/html/2407.11484v4#S1 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  2. [2 Background](https://arxiv.org/html/2407.11484v4#S2 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [2.1 PLMs and LLMs](https://arxiv.org/html/2407.11484v4#S2.SS1 "In 2 Background ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [2.2 Key Components](https://arxiv.org/html/2407.11484v4#S2.SS2 "In 2 Background ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  3. [3 Data](https://arxiv.org/html/2407.11484v4#S3 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [3.1 Data Objectives](https://arxiv.org/html/2407.11484v4#S3.SS1 "In 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [3.2 Persona-based Role-Playing Datasets](https://arxiv.org/html/2407.11484v4#S3.SS2 "In 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [Interaction Collection](https://arxiv.org/html/2407.11484v4#S3.SS2.SSS0.Px1 "In 3.2 Persona-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [Role-related Information.](https://arxiv.org/html/2407.11484v4#S3.SS2.SSS0.Px2 "In 3.2 Persona-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    3. [3.3 Character-based Role-Playing Datasets](https://arxiv.org/html/2407.11484v4#S3.SS3 "In 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [Interaction Collection](https://arxiv.org/html/2407.11484v4#S3.SS3.SSS0.Px1 "In 3.3 Character-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [Role-related Information.](https://arxiv.org/html/2407.11484v4#S3.SS3.SSS0.Px2 "In 3.3 Character-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  4. [4 Models and Alignment](https://arxiv.org/html/2407.11484v4#S4 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [4.1 Foundation Models](https://arxiv.org/html/2407.11484v4#S4.SS1 "In 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [Non-pretrained models and PLM.](https://arxiv.org/html/2407.11484v4#S4.SS1.SSS0.Px1 "In 4.1 Foundation Models ‣ 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [LLM.](https://arxiv.org/html/2407.11484v4#S4.SS1.SSS0.Px2 "In 4.1 Foundation Models ‣ 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [4.2 Alignment](https://arxiv.org/html/2407.11484v4#S4.SS2 "In 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [4.2.1 Parameter-Tuning Alignment](https://arxiv.org/html/2407.11484v4#S4.SS2.SSS1 "In 4.2 Alignment ‣ 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [4.2.2 Parameter-Frozen Alignment](https://arxiv.org/html/2407.11484v4#S4.SS2.SSS2 "In 4.2 Alignment ‣ 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      3. [4.2.3 Summary and Discussion.](https://arxiv.org/html/2407.11484v4#S4.SS2.SSS3 "In 4.2 Alignment ‣ 4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  5. [5 Agent Architecture](https://arxiv.org/html/2407.11484v4#S5 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [5.1 Memory](https://arxiv.org/html/2407.11484v4#S5.SS1 "In 5 Agent Architecture ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [5.2 Planning](https://arxiv.org/html/2407.11484v4#S5.SS2 "In 5 Agent Architecture ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    3. [5.3 Action](https://arxiv.org/html/2407.11484v4#S5.SS3 "In 5 Agent Architecture ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    4. [5.4 Summary and Discussion](https://arxiv.org/html/2407.11484v4#S5.SS4 "In 5 Agent Architecture ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  6. [6 Evaluation](https://arxiv.org/html/2407.11484v4#S6 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [6.1 Evaluation Perspectives](https://arxiv.org/html/2407.11484v4#S6.SS1 "In 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [6.1.1 Conversation Ability](https://arxiv.org/html/2407.11484v4#S6.SS1.SSS1 "In 6.1 Evaluation Perspectives ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [6.1.2 Role-Persona Consistency](https://arxiv.org/html/2407.11484v4#S6.SS1.SSS2 "In 6.1 Evaluation Perspectives ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      3. [6.1.3 Role-Behavior Consistency](https://arxiv.org/html/2407.11484v4#S6.SS1.SSS3 "In 6.1 Evaluation Perspectives ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      4. [6.1.4 Role-Playing Attractiveness](https://arxiv.org/html/2407.11484v4#S6.SS1.SSS4 "In 6.1 Evaluation Perspectives ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      5. [6.1.5 Summary and Discussion](https://arxiv.org/html/2407.11484v4#S6.SS1.SSS5 "In 6.1 Evaluation Perspectives ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [6.2 Evaluation Approaches](https://arxiv.org/html/2407.11484v4#S6.SS2 "In 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      1. [6.2.1 Reference-based Evaluation](https://arxiv.org/html/2407.11484v4#S6.SS2.SSS1 "In 6.2 Evaluation Approaches ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      2. [6.2.2 Human-based Evaluation](https://arxiv.org/html/2407.11484v4#S6.SS2.SSS2 "In 6.2 Evaluation Approaches ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      3. [6.2.3 LLM-based Evaluation](https://arxiv.org/html/2407.11484v4#S6.SS2.SSS3 "In 6.2 Evaluation Approaches ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      4. [6.2.4 Evaluating RPLAs](https://arxiv.org/html/2407.11484v4#S6.SS2.SSS4 "In 6.2 Evaluation Approaches ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
      5. [6.2.5 Summary and Discussion](https://arxiv.org/html/2407.11484v4#S6.SS2.SSS5 "In 6.2 Evaluation Approaches ‣ 6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  7. [7 Challenges and Future Directions](https://arxiv.org/html/2407.11484v4#S7 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    1. [More Reference-based Metrics for evaluating Role-Playing.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px1 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    2. [Sensitivity in LLM-Based Evaluation.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px2 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    3. [Imbalance, Bias and Cost in Human-based Evaluation.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px3 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    4. [Lack of deeper Role-specific Alignment Approaches.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px4 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    5. [Ensure the safety in Role-Playing.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px5 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    6. [Hallucination in Role-Playing.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px6 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    7. [Memory Usage in RPLAs.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px7 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    8. [Multi-Modal Integration in RPLAs.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px8 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    9. [Make RPLAs be lifelong learner.](https://arxiv.org/html/2407.11484v4#S7.SS0.SSS0.Px9 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
    10. [7.1 Summary and Discussion](https://arxiv.org/html/2407.11484v4#S7.SS1 "In 7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models")
  8. [8 Conclusion](https://arxiv.org/html/2407.11484v4#S8 "In The Oscars of AI Theater: A Survey on Role-Playing with Language Models")



# The Oscars of AI Theater: A Survey on Role-Playing with Language Models

Nuo Chen♠ Yang Deng♢ Jia Li♠   
  
♠Hong Kong University of Science and Technology (Guangzhou)   
Hong Kong University of Science and Technology   
♢Singapore Management University   
nchen022@connect.ust.hk, jialee@ust.hk   


###### Abstract

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>.

The Oscars of AI Theater: A Survey on Role-Playing with Language Models

  


Nuo Chen♠ Yang Deng♢ Jia Li♠ ♠Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology ♢Singapore Management University nchen022@connect.ust.hk, jialee@ust.hk

  


##  1 Introduction

Today, most large language models (LLMs) Brown et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib8)); Hu et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib36)); Zeng et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib126)); OpenAI ([2023](https://arxiv.org/html/2407.11484v4#bib.bib62)); Scao et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib78)) are proficient enough to act as assistants, but the ever-expanding desires of humans gradually go beyond this role. A helpful but serious assistant isn’t everything in human life. An increasing number of individuals have been instructing LLMs to take on roles they desire, such as movie stars, game characters, or even their own relatives. This practice of aligning LLMs with specific personas or characters is commonly known as Role-Playing Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)); [Jiang et al. ](https://arxiv.org/html/2407.11484v4#bib.bib42); Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Qian et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib68)); Chen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib12)). If the standard assistant role of LLMs meets the demand for increased productivity, then LLMs for role-playing aims to fulfill human needs at a psychological and entertainment level. This lively trend underscores the versatility of LLMs and the limitless potential of human imagination in the realm of artificial intelligence.

The requirements for role-playing with language models differ significantly from those of a generic assistant. The primary expectation from a generic assistant is its helpfulness, meaning the LLM should follow the user’s instructions and provide the desired responses Serban et al. ([2016](https://arxiv.org/html/2407.11484v4#bib.bib80)); Lowe et al. ([2015](https://arxiv.org/html/2407.11484v4#bib.bib55)); Thoppilan et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib95)); Miller et al. ([2017](https://arxiv.org/html/2407.11484v4#bib.bib59)); You et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib123)). This expectation is also evident in the benchmarks for such models: people often desire an assistant with extensive professional knowledge and strong logical reasoning abilities.

However, when the task comes to role-playing, the most crucial criterion is the LLM’s ability to align with specific personas or characters Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99), [2023](https://arxiv.org/html/2407.11484v4#bib.bib98)); Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). In other words, humans expect the LLMs to interact with them in a manner consistent with a specific role. This expectation introduces a fascinating dynamic that can sometimes contradict the traditional notion of helpfulness. For instance, consider a scenario where the role to be played is that of the user’s adversary or enemy. In such a case, being helpful becomes a contradictory metric. The more helpful the model is, the less effective it becomes at role-playing.

In the era marked by the advent of sequence to sequence learning Shang et al. ([2015](https://arxiv.org/html/2407.11484v4#bib.bib82)), researchers began exploring the potential of neural networks to generate dialogue responses that are consistent with both the given context and the portrayed persona Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)); [Jiang et al. ](https://arxiv.org/html/2407.11484v4#bib.bib42); Dinan et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib25)). For instance, Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)) utilizes a generative profile memory network to generate personal responses. This initial motivation laid the groundwork for the following role-playing works. Subsequent advancements with the introduction of models such as BERT Kenton and Toutanova ([2019](https://arxiv.org/html/2407.11484v4#bib.bib43)) brought significant evolutions to the use of language models for role-playing, despite certain inherent limitations. In this period, due to the improved but still developing generative capabilities of these pre-trained language models (PLMs) Liu et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib54)); Raffel et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib72)), role-playing is largely focused on achieving persona consistency through simpler, more straightforward persona roles, often termed as persona-based role-playing. This is partly due to the nature of the personalized information available in datasets at the time, which was often succinct and sparse, as seen in resources like the Persona-Chat dataset Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)). PLMs are fine-tuned on these datasets to produce responses that aligned with the limited personal information provided, striving for a balance between consistent understanding and competent dialogue generation.

As the field progressed into the era of LLMs OpenAI ([2023](https://arxiv.org/html/2407.11484v4#bib.bib62)); Touvron et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib97)); Zeng et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib126)), a paradigm shift occurred. The enhanced comprehension and generative abilities of LLMs expand the scope of role-playing tasks far beyond simple persona adherence. Current research in role-playing no longer confines itself to rigid personas. Instead, it delves into more nuanced aspects of role enactment, such as character consistency, behavioral alignment, and overall attractiveness of the character portrayal Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)), named as character-based role-playing. These dimensions aim to create more immersive and believable character simulations that maintain continuity over interactions and adapt dynamically to dialogue contexts. The progress of LLM-based role-playing leading to a rapid expansion in academic research and the development of practical applications, exemplified by platforms like Character AI111https://character.ai/, Xingye222https://www.xingyeai.com/, and Maopaoya333https://maopaoya.com/.

Despite the promising potential of role-playing with language models, research in this domain remains in its early stages, marked by both complexities and challenges. The goal of this survey is to understand the crucial mechanisms and methodologies that enable role-playing through text-based interactions. To achieve a thorough understanding, we introduce a detailed taxonomy to systematically examine the critical components involved in designing role-playing language models. The proposed taxonomy includes: Data, Models & Alignment, Agent Architecture, and Evaluation. This framework aims to not only detail how role-playing functions within these systems but also to highlight how it can be optimized and evaluated for a variety of applications.

In summary, §[1](https://arxiv.org/html/2407.11484v4#S2.F1 "Figure 1 ‣ 2 Background ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") first introduces the preliminary of our survey, like the evolution of language models and key components. §[3](https://arxiv.org/html/2407.11484v4#S3 "3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") provides a comparative overview of the current role-playing data resources, highlighting their unique characteristics and applicability in different scenarios. For models & alignment, §[4](https://arxiv.org/html/2407.11484v4#S4 "4 Models and Alignment ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") systematically reviews the foundational models of role-playing and summarizes the past alignment approaches, offering insights into their strengths and weaknesses. For Agent architecture, §[5](https://arxiv.org/html/2407.11484v4#S5.F5 "Figure 5 ‣ 5 Agent Architecture ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") details the important modules that impact the effectiveness and generalization of role-playing language model agents, including memory, planning, action. Then, §[6](https://arxiv.org/html/2407.11484v4#S6 "6 Evaluation ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") compiles comprehensive evaluation standards and metrics, encompassing both subjective and objective metrics, along with their respective advantages and disadvantages. Finally, §[7](https://arxiv.org/html/2407.11484v4#S7 "7 Challenges and Future Directions ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") delves into the 10 main challenges that persist in role-playing and envisages possible solutions that could pave the way for more advanced and nuanced systems.

##  2 Background

![Refer to caption](x1.png) Figure 1: Key components in role-playing with language models. 

###  2.1 PLMs and LLMs

Despite the absence of a universally accepted definition for Large Language Models (LLMs), this paper proposes a specific delineation for LLMs as referenced within our analysis. Distinguished by both model scale and training methodologies, our definition builds upon distinctions made by two seminal surveys on LLMs (Zhao et al., [2023b](https://arxiv.org/html/2407.11484v4#bib.bib131); Yang et al., [2023](https://arxiv.org/html/2407.11484v4#bib.bib120)), differentiating LLMs from earlier Pre-trained Language Models (PLMs) based on the magnitude of model parameters and the scope of pre-training data. Specifically, LLMs refer to expansive models with parameters in the billions, pre-trained on extensive datasets, in contrast to PLMs, which are characterized by their relatively moderate parameter sizes in the millions and the capability for efficient task-specific fine-tuning to enhance performance on downstream tasks.

Notable PLMs include BERT Kenton and Toutanova ([2019](https://arxiv.org/html/2407.11484v4#bib.bib43)), GPT-2 Radford et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib70)), BART Lewis et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib46)), and Roberta Liu et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib54)), whereas leading examples of LLMs, such as GPT-3 (Brown et al., [2020](https://arxiv.org/html/2407.11484v4#bib.bib8)), PaLM (Chowdhery et al., [2022](https://arxiv.org/html/2407.11484v4#bib.bib18)), Mistral Jiang et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib41)), and LLaMA (Touvron et al., [2023](https://arxiv.org/html/2407.11484v4#bib.bib97)), primarily utilize decoder-only architectures. The progression in architectural and training innovations for LLMs has facilitated the emergence of advanced capabilities (Wei et al., [2022b](https://arxiv.org/html/2407.11484v4#bib.bib110)), empowering them to tackle complex problems in few-shot or zero-shot settings through methodologies like in-context learning (Radford et al., [2021](https://arxiv.org/html/2407.11484v4#bib.bib69); Dong et al., [2022](https://arxiv.org/html/2407.11484v4#bib.bib27)) and chain-of-thought reasoning (Wei et al., [2022c](https://arxiv.org/html/2407.11484v4#bib.bib111)). After reading this review, you will find that, in fact, the capabilities of zero-shot and few-shot learning are the main reasons for the increasing attention and interest in the domain of role-playing over the past two years.

###  2.2 Key Components

Employing language models for role-playing involves several critical factors that significantly influence their effectiveness and personalization capabilities. In this survey, we analyze several components that are essential in shaping the development, deployment, and continuous improvement of role-playing language models:

  * •

Data: The diversity and complexity of the data used in role-playing are foundational, influencing the models’ ability to generate authentic and personalized interactions. Role-related information in current datasets ranges from structured texts and simple sentences to rich compilations of detailed personal information like attributes, relationships, and even nuanced understandings of characters across different timelines. The sophistication of a language model in handling role-playing scenarios directly correlates with the complexity of the targeted dataset. Models trained on simpler data might generate broad, generic responses, while those trained on more complex datasets are capable of producing dynamic dialogues that reflect specific character nuances. Hence, the selection and design of the dataset are critical in role-playing, directly impacting effectiveness in delivering engaging and personalized conversational experiences.

  * •

Models & Alignment: Undoubtedly, the backbone models play a pivotal role in the role playing’s operational efficiency. From traditional neural networks to language models, the choice of model influences the system’s understanding, generation, and adaptation capabilities. The recent advent of LLMs has brought significant advancements in this area, achieving high levels of personalization and coherence. 

Meanwhile, alignment approaches focus on ensuring that the role-playing models’ responses align with the intended role. These methods range from rule-based systems that manually map responses to personas, to dynamic learning mechanisms that adapt responses based on interaction history and persona data. Technically, we divide them into Parameter-Tuning: Post-training, Supervised Fine-Tuning (SFT), and Reinforcement Learning; and Parameter-Frozen: In-context learning prompting and Retrieval-Augmented Generation (RAG).

  * •

Agent Architecture: Currently, the development of Role-Playing Language Agents (RPLAs) marks a new evolution. These agents extend the basic framework of role-playing by integrating both interactive and autonomous behaviors, enabling them to not only personify specific characters but also engage proactively in complex and evolving scenarios. Effective RPLAs require a comprehensive system architecture that includes several key modules: memory, for recalling and utilizing past interactions; planning, for strategic decision-making; and action, which encompasses both generating role-related responses and using tools. Such complex architectures ensure RPLAs are not only interactive but also adaptive and context-aware, essential for complex role-playing scenarios. 

  * •

Evaluation: Evaluating the performance of role-playing models is crucial for assessing their effectiveness and guiding improvements. Commonly, role-playing evaluation involves a complex array of perspectives that extend beyond those applied to traditional dialogue systems, focusing on role consistency, engagement, human-likeness, and proactivity, among others. This complexity necessitates a diverse set of evaluation metrics, encompassing both subjective human-based assessments and objective reference-based. The advent of LLMs has also spurred the development of LLM-based evaluation methods, offering an efficient alternative to conventional human annotation by approximating human judgments. However, given the multifaceted nature of role-playing, no single metric suffices to fully assess their performance. Thus, a composite approach, utilizing multiple metrics in tandem, is essential for a comprehensive evaluation.




In the following sections, we present a comprehensive survey along with our taxonomy.

##  3 Data

{forest} forked edges, for tree= grow=east, reversed=true, anchor=base west, parent anchor=east, child anchor=west, base=left, font=, rectangle, draw=hidden-black, rounded corners, align=left, minimum width=4em, edge+=darkgray, line width=1pt, s sep=3pt, inner xsep=2pt, inner ysep=3pt, line width=0.8pt, ver/.style=rotate=90, child anchor=north, parent anchor=south, anchor=center, , where level=1text width=4.1em,font=,, where level=2text width=7.2em,font=,, where level=3text width=7.2em,font=,, where level=4text width=6.4em,font=,, [ Data, ver, text=black [ Persona-based , text=black [ Employing Crowdsourcing   
Workers, text=black [ _e.g._ Persona-Chat Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)), Focus Jang et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib39)), KvPI Song et al. ([2020b](https://arxiv.org/html/2407.11484v4#bib.bib92)),   
WOW Dinan et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib25)), ConvAI2 Dinan et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib24)), detect_leaf, text width=27.2em ] ] [ Extracting from Social   
Media, text=black [ _e.g._ Pchatbot Qian et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib68)), PeDialog Zheng et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib132)), LiveChat Gao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib30)),   
P-Ubuntu Li et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib50)), Friends-QA Yang and Choi ([2019](https://arxiv.org/html/2407.11484v4#bib.bib121)) , detect_leaf, text width=27.2em ] ] ] [ Character-based, text=black [ LLMs as Data Generator, text=black [ _e.g._ InCharacter Wang et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib105)), RoleInteract Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)), Rolebench Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)),   
CharacterDia Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), MBTI-S2Conv Tu et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib98)) , detect_leaf, text width=27.2em ] ] [ Extracting from   
Literary Resources, text=black [ _e.g._ HPD Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), CharacterEval Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)), RoleEval Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)),   
CharacterDia Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Timechara Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)) , detect_leaf, text width=27.2em ] ] [ Unpublished Resources, text=black [ _e.g._ Dolphin Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), RoleEval Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)), PIPPA Gosling et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib34)) , detect_leaf, text width=27.2em ] ] [ Human Role-Playing, text=black[_e.g._ CharacterDia Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)) , detect_leaf, text width=27.2em] ] ] ]

Figure 2: The main content flow and categorization of Data Section. 

###  3.1 Data Objectives

Unlike traditional text generation tasks, the capabilities of a role-playing language model are primarily determined by the target dataset it is fitted on. Therefore, the dataset is the most crucial prerequisite for training, categorizing, and testing different role-playing dialogue agents. Commonly, role-playing datasets contain two important components: interactions and role-related information. It’s worth noting that we use the term "interactions" instead of the commonly used "conversations" or "dialogues". This is because we believe that the essence of role-playing lies in mimicking the behavior of the role in any scenarios, not just in dialogues. The reason why most current research is limited to the conversation-level is that, compared to other scenarios, the user’s actions within a conversation are the easiest to obtain.

In this study, based on the different objectives of the targeted datasets, we categorize role-playing applications into two types: Persona-based Role-Playing (P-RP) and Character-based Role-Playing (C-RP). Generally, P-RP means that the dataset contains coarse-grained role-related information, whereas building C-RP requires fine-grained role-related information. The classification of granularity of coarse-grained or fine-grained primarily depends on whether the role-related information includes character-level specific background details. Datasets lacking such details are deemed coarse-grained, whereas those containing them are considered fine-grained. Here, we further emphasize the differences:

  * •

Persona-based Role-Playing (P-RP): These role-playing scenarios mimic broad categories of personas, focusing primarily on superficial and common attributes like location and gender. They are expected to display characteristics of specific groups of people based on given coarse-gained personas Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)); Dinan et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib25)); Kottur et al. ([2017](https://arxiv.org/html/2407.11484v4#bib.bib44)); Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)). P-RP is simpler, designed to ensure consistency within a narrower set of persona traits. They are generally less complex and tailored for generic roles that require basic interaction capabilities.

  * •

Character-based Role-Playing (C-RP): In contrast, C-RP scenarios are crafted to emulate specific characters from various narratives, such as novels, movies or even celebrities. These involves incorporating fine-grained character-level personal background information, including attributes, complex relationships, scene and nuanced psychological states Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16), [2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)). C-RP with language models is inherently complex, aimed at achieving deep, role-specific interactions and are equipped with features such as human-likeness, empathy, and proactivity Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)). They are designed to offer a more immersive and engaging user experience.




Note, in the evolving landscape of role-playing research, Persona-based Role-Playing (P-RP) is seen as a specific subset within the broader spectrum of Character-based Role-Playing (C-RP). Currently, the focus is increasingly on C-RP, reflecting a shift toward more complex and nuanced scenarios capable of leveraging the sophisticated capabilities of curret LLMs. This trend underscores a growing interest in developing role-playing interactions that offer rich, character-driven experiences. We classify different datasets according to interaction collection in Figure [2](https://arxiv.org/html/2407.11484v4#S3.F2 "Figure 2 ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models") and present overview of existing datasets in Table [1](https://arxiv.org/html/2407.11484v4#S3.T1 "Table 1 ‣ Role-related Information. ‣ 3.2 Persona-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models").

###  3.2 Persona-based Role-Playing Datasets

In general, the datasets associated with persona-based data tend to provide personas that are coarse-grained. The collection and construction of appropriate training data have become essential prerequisites for enabling chit-chat dialogue agents to generate persona-specific responses.

##### Interaction Collection

According to how to collect role-related conversations, we can generally classify the data collection process into two main streams:

  * •

Employing Crowdsourced Workers: This method involves hiring crowdworkers to create the corresponding dialogue datasets. Initially, certain personas and related topics are manually defined. Then, crowdworkers engage in dialogues based on these provided personas, resulting in personalized dialogues. The advantage of this approach lies in the guaranteed high quality of the data; however, due to the cost of manual labor, the scale is often limited. Notable examples include the Persona-Chat dataset Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)), which contains about 10,000 persona dialogues, and the Focus Jang et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib39)) dataset with approximately 14,000 conversations.

  * •

Extracting from Social Media: This method typically involves collecting a large volume of user dialogue data from social media platforms and applying specific filtering rules to obtain the final dataset. The strengths of these datasets are that they reflect real-world personalized dialogues and are large in scale. For instance, PersonalDialog Zheng et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib132)) and Pchatbot Qian et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib68)) have respectively gathered over 20 million and 130 million dialogue sessions from Weibo. However, a significant drawback is the difficulty in controlling data quality.




In general, there is a trade-off between the above two collection processes: The former provides high-quality, controlled data at a smaller scale, while the latter offers extensive real-world dialogue data, albeit with potential quality control issues.

##### Role-related Information.

Dataset Scene Attributes Relations Temporal Source Usage Annotation Language Persona-based Pchatbot Qian et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib68)) ×\times× √square-root\surd√ ×\times× ×\times× Weibo Train & Test Rule Zh PersonalDialog Zheng et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib132)) ×\times× √square-root\surd√ ×\times× ×\times× Weibo Train & Test Rule Zh KvPI Song et al. ([2020b](https://arxiv.org/html/2407.11484v4#bib.bib92)) ×\times× √square-root\surd√ ×\times× ×\times× Weibo Train & Test Human Zh Personal-Chat Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)) ×\times× √square-root\surd√ ×\times× ×\times× Crowdsourcing Train & Test Human En RealPersonaChat Yamashita et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib119)) ×\times× √square-root\surd√ ×\times× ×\times× Crowdsourcing Train & Test Human JP COMSET Agrawal et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib2)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Comics Train & Test Tool-Human En WOW Dinan et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib25)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Wikipedia Train & Test Human En Fri-QA Yang and Choi ([2019](https://arxiv.org/html/2407.11484v4#bib.bib121)) ×\times× √square-root\surd√ √square-root\surd√ ×\times× TV-Show Train & Test Human En Focus Jang et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib39)) ×\times× √square-root\surd√ ×\times× ×\times× AMT Train & Test Human En UltraChat Ding et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib26)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Real-World Train & Test LLM En LiveChat Gao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib30)) ×\times× √square-root\surd√ ×\times× ×\times× Live Videos Train & Test LM-Human Zh Cho et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib17)) ×\times× √square-root\surd√ ×\times× ×\times× Crowdsourcing Train & Test Human KR Character-based LaMP Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Existing Corpus Test - En CharacterEval Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Novels Test LLM-human Zh RoleInteract Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)) √square-root\surd√ √square-root\surd√ √square-root\surd√ ×\times× Multi-Sources Test LLM-Human En-Zh RoleEval Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)) √square-root\surd√ √square-root\surd√ √square-root\surd√ √square-root\surd√ Multi-Sources Test LLM-Human En-Zh TimeChara Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)) √square-root\surd√ √square-root\surd√ √square-root\surd√ √square-root\surd√ Novels Test LLM En Cross-MR Yuan et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib125)) √square-root\surd√ √square-root\surd√ √square-root\surd√ ×\times× Multi-Sources Test LLM En LifeChoice Xu et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib116)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Novels Test LLM-Human En PIPPA Gosling et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib34)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Character.AI Train - En Character100 Wang et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib104)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Novels Train & Test LLM En HPD Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)) √square-root\surd√ √square-root\surd√ √square-root\surd√ √square-root\surd√ Novels Train & Test LLM-Human En-Zh RoleBench Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)) ×\times× √square-root\surd√ ×\times× ×\times× Scripts Train & Test LLM En-Zh CharacterDia Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)) √square-root\surd√ √square-root\surd√ √square-root\surd√ ×\times× Multi-Sources Train & Test LLM-Human Zh ChatHaruhi Li et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib47)) ×\times× √square-root\surd√ ×\times× ×\times× Literary Sources Train & Test LLM En-Zh MBTI-S2Conv Tu et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib98)) ×\times× √square-root\surd√ ×\times× ×\times× MBTI Train & Test LLM En Prodigy Occhipinti et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib61)) ×\times× √square-root\surd√ ×\times× ×\times× Literary Sources Train & Test LLM-Human En Ditto Lu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib56)) ×\times× √square-root\surd√ ×\times× ×\times× Open Sources Train & Test LLM En-Zh Character-LLM Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)) √square-root\surd√ √square-root\surd√ ×\times× ×\times× Wikipedia Train & Test LLM En

Table 1: Overview of existing datasets for role-playing. 

Role-related information in P-RP datasets is crucial for generating realistic and context-appropriate responses, and it can be categorized into two distinct forms: explicit and implicit. Explicit information refers to instances where each interaction is accompanied by a detailed persona, presented either in a natural language format Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)) or a structured format Song et al. ([2020b](https://arxiv.org/html/2407.11484v4#bib.bib92)); Zheng et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib132)). A prime example of the former, a persona in natural language format, is as follows (sourced from paper Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127))):

RPGs are my favorite genre.

I also went to school to work with technology.

The woman who gave birth to me is a physician.

I am not a social person.

I enjoy working with my hands.

As for the structured format, an example from (Zheng et al., [2019](https://arxiv.org/html/2407.11484v4#bib.bib132)) is as follows:

{Age: Post-90s,

Gender: Female,

Location: Beijing, 

Constellation: Aquarius}

To the best of our knowledge, most real-world role-playing applications incorporate both formats of role-related information. The key-value structured format is utilized to provide common, fundamental details that are necessary for each role, such as height, weight, and gender. On the other hand, the natural language format is employed to convey unique background information, experiences, and catchphrases specific to the role, which are challenging to encapsulate within the confines of a structured format.

In the context of implicit personas, as referenced in sources such as Li et al. ([2016](https://arxiv.org/html/2407.11484v4#bib.bib49)); Zhang et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib129)), some might contend that they do not fall within the scope of role-playing, given that they do not provide any role information. Such datasets distinguish dialogues belonging to different roles but do not supply persona information for each role. We still consider these works to be part of the role-playing domain, as the role-related information can essentially be inferred by summarizing the interaction history. We categorize these works under "Implicit persona". Undoubtedly, utilizing these types of datasets for role-playing is a more challenging task, as we must first devise methods to extract accurate role-related information from the interaction history.

###  3.3 Character-based Role-Playing Datasets

As LLMs’ comprehension capabilities have improved, both researchers and users have found that coarse-grained personas are no longer sufficient to meet their needs for entertainment and psychological engagement. They have started to use LLMs to ‘recreate’ their favorite characters, a practice known as Character-based Role Playing. Typically, the role-related information provided by Character-based Role-playing can be a comprehensive role description (comprising thousands of tokens), or it could be corpus-level background materials such as novels or narratives. A pioneering effort in this field is the HPD dataset Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), a dataset based on the Harry Potter novels, which is used to train LLMs to align with the Harry Potter.

##### Interaction Collection

Character-based role-playing scenarios involve simulating a broad spectrum of roles, categorized mainly into two categories: real world-based and virtual scenario-based. Real world-based roles often mimic actual celebrities or typical individuals from daily life, while virtual scenario-based roles draw from fictional sources like novels, TV series, video games, and even theoretical constructs such as MBTI personalities Wang et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib105)). The diversity of these roles necessitates equally diverse methodologies for data collection, each with its unique set of challenges and solutions:

  * •

LLM as Data Generator: With the advanced generative capabilities of models like GPT-4, LLMs serve as a primary tool for synthesizing character profiles and dialogues. Related implementations include: 1) Generating complete character profiles from scratch and using these profiles to prompt varied character-based dialogues Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)); 2) Employing established profiles from resources like MBTI or Wikipedia as a basis for generating personalized dialogues Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)); Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)); Lu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib56)). While effective, LLM-based data generation can introduce biases and unpredictable variations in data quality, necessitating careful manual review and validation Anwar et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib5)).

  * •

Extracting from Literary Resources: This method involves the extraction of role-related conversations and character backgrounds directly from literary sources, particularly for fictional characters depicted in novels and television Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)); Zhao et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib130)). However, this approach faces several challenges: 1) Dialogues are often tied to specific scenes, making it complex to delineate the scene of the dialogue and to extract the relevant textual context. 2) Automatically extracting detailed background information about characters is difficult. 3) Identifying multiple statements made by a character within a dialogue round can be complex. 4) Some dialogues involve non-verbal cues and contexts that are difficult to convey textually. A common solution involves using LLMs, human annotators, or a combination of both. For instance, Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)) utilized a group of professional annotators and the LLM tools to annotate dialogue, attributes, and relationships from the Harry Potter novels.

Undoubtedly, the language quality of the interactions extracted from this source is exceptional, and they exhibit the highest degree of alignment, given that the characteristics of the roles are originally defined by these works. However, the primary challenge lies in the significant disparity between the dialogue style in the literaries and the daily user-AI dialogue style. This discrepancy often results in the trained models underperforming in interactions with real users.

  * •

Human Role-Playing: Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)) and Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)) hire different crowdworkers who are given specific character profiles to role-play, and then engage in interactions. This data generation method generally produces high-quality data but lacks diversity and is costly.

  * •

Unpublished Resources: All the aforementioned acquisition methods are sourced from formal, published academic papers. However, according to our practical experience, solely relying on these datasets is inadequate to train a product-level role-playing model that fulfills user expectations. Consequently, we present a list of higher-quality role-playing data sources. However, it’s important to note: we do not guarantee the legality of these data resources, and the developers should confirm any potential legal risks by themselves.

The first type of resource is role-play forums, which contain a vast amount of human-human role-playing data. Some well-known forums include Blue Moon444https://bluemoonroleplaying.com/community/, NationStates555https://forum.nationstates.net/, Aryion666https://aryion.com/, Questionable Questing777https://forum.questionablequesting.com/, Role-Player888https://role-player.net/forum/blog.php, and Spacebattles999https://forums.spacebattles.com/. It’s important to note that these forums often contain adult-only content, rigorous data cleaning is required before use. The second type is the log of some online role-playing products Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11), [d](https://arxiv.org/html/2407.11484v4#bib.bib14)) such as CharacterAI101010https://character.ai/. But the use of such data requires dual authorization from both the users and the product developers. The last type is fanfiction communities, such as AO3111111https://archiveofourown.org/. For some well-known characters like Harry Potter, the volume of fanfiction is thousands of times that of the original work. However, the risk lies in the fact that there are many Out-of-Character situations in fanfiction, as authors will add many personas according to their own preferences.




![Refer to caption](x2.png) Figure 3: Typical (a) persona-based and (b) character-based role-related information in role-playing datasets. The given examples (a) are from Persona-Chat Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)) and PersonalDialog Zheng et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib132)). Example (b) is collected from HPD Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). 

##### Role-related Information.

Character-based role-playing needs character-level fine-grained personal background information, which encompasses a range of elements, as shown in Figure [3](https://arxiv.org/html/2407.11484v4#S3.F3 "Figure 3 ‣ Interaction Collection ‣ 3.3 Character-based Role-Playing Datasets ‣ 3 Data ‣ The Oscars of AI Theater: A Survey on Role-Playing with Language Models"). Below is a detailed categorization of the typical personal content found within these datasets:

  * •

Specific Scenes: This category captures detailed contextual information about the dialogues, including the precise timing, location, and underlying reasons Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)); Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). Such data is essential for situating the dialogue within a clear narrative frame, thereby providing a more immersive experience.

  * •

Comprehensive Attributes: Attributes cover a wide spectrum of personal details about the characters Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)); Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), including the name, gender, personality, age, identity, title, affiliation, quotes, belongings, etc.

  * •

Complex relationships: Relationship is another key perspective to make role-playing models delve deeper into the social and emotional landscapes of the specific characters Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11), [2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), which contains opponents, familiarity, and family details, etc.

  * •

Temporal Personas: This aspect acknowledges that characters are not static; their personal information can evolve over time, reflecting developments in the character’s storyline or changes in their environment. This temporal dimension allows for the simulation of growth and transformation, providing a richer narrative Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)); Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)).




Each category of data plays a critical role in constructing nuanced and responsive character-level role-playing models which can interact in a manner that mirrors human-like complexity and depth. Note, not all datasets contain each of the aforementioned elements. For instance, CharacterEval Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)) provides scene and attributes while RoleInteract Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)) contains scene, attributes and relations.

##  4 Models and Alignment

{forest}

forked edges, for tree= grow=east, reversed=true, anchor=base west, parent anchor=east, child anchor=west, base=left, font=, rectangle, draw=darkpastelgreen, rounded corners, align=left, minimum width=4em, edge+=darkgray, line width=1pt, s sep=3pt, inner xsep=2pt, inner ysep=3pt, ver/.style=rotate=90, child anchor=north, parent anchor=south, anchor=center, , where level=1text width=5.2em,font=,, where level=2text width=5.5em,font=,, where level=3text width=7.2em,font=,, where level=4text width=6.4em,font=,, [ Modeling and Alignment, ver, color=darkpastelgreen!100, text=black [ Foundation Models, color=darkpastelgreen!100, text=black[ Non-pretrained, color=darkpastelgreen!100, text=black [ _e.g._ Per-S2SA Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)), DeepCopy Yavuz et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib122)), Per-CVAE Song et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib93)), GDR Song et al. ([2020a](https://arxiv.org/html/2407.11484v4#bib.bib91)) , cause_leaf, text width=36.2em ] ] [ PLM-based, color=darkpastelgreen!100, text=black [ _e.g._ ConvAI2 Dinan et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib24)), LIC Golovanov et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib33)), BOB Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)), Attention Routing Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)) , cause_leaf, text width=36.2em ] ] [ LLM-based, color=darkpastelgreen!100, text=black [ _e.g._ CharacterGLM Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Character-LLM Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)), RoleLLM Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), ChatHaruhi Li et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib47))   
ChatPlug Tian et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib96)), DITTO Lu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib56)) , cause_leaf, text width=36.2em ] ] ] [ Alignment, color=darkpastelgreen!100, text=black[ Parameter-Tuning, color=darkpastelgreen!100, text=black [ Continue-Pretrain, color=darkpastelgreen!100, text=black [ _e.g._ ChatPlug Tian et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib96)), Huang et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib38)), mitigate_leaf, text width=27.3em ] ] [ Supervised Fine-tuning , color=darkpastelgreen!100, text=black [ _e.g._ RoCIT Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), BOB Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)), RoleLLM Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106))   
CharacterGLMZhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Character-LLM Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)), PLATO-LTM Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)), mitigate_leaf, text width=27.3em ] ] [ Self-Alignment, color=darkpastelgreen!100, text=black [ _e.g._ CharacterGLM Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), Lu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib56)), mitigate_leaf, text width=27.3em ] ] [ Parameter-Efficiency   
Tuning, color=darkpastelgreen!100, text=black [ _e.g._ PersonaPKT Han et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib35)), Neeko Yu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib124)), mitigate_leaf, text width=27.3em ] ] [ Reinforcement Learning , color=darkpastelgreen!100, text=black [ _e.g._ COMEDY Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), Shea and Yu ([2023](https://arxiv.org/html/2407.11484v4#bib.bib84)), mitigate_leaf, text width=27.3em] ] ] [ Parameter-Frozen, color=darkpastelgreen!100, text=black [ In-context Learning   
Prompting, color=darkpastelgreen!100, text=black [ _e.g._ Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), Shea and Yu ([2023](https://arxiv.org/html/2407.11484v4#bib.bib84)), Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)), mitigate_leaf, text width=27.3em ] ] [ Retrieval Augmented   
Generation, color=darkpastelgreen!100, text=black [_e.g._ RECAPLiu et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib53)), NarrativeplayShea and Yu ([2023](https://arxiv.org/html/2407.11484v4#bib.bib84)), LAMP Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)) , mitigate_leaf, text width=27.3em] ] ] ] ]

Figure 4: The main content flow and categorization of Models and Alignment. 

###  4.1 Foundation Models

Foundation models are critical in setting the base capabilities of role-playing models. As the underlying architectures, they determine the lower bounds of performance and sophistication achievable in role-playing scenarios. The development of foundation models and architectures for role-playing can be viewed as a progressive evolution across three distinct stages: non-pretrained model, PLM, and LLM. Each stage represents a significant shift in backbone selection for role-playing models.

##### Non-pretrained models and PLM.

The earliest stage in the development of role-playing models involved non-pretrained architectures. These models are crafted from scratch, tailored to specific tasks without the benefit of large-scale, pre-trained data. Early models often utilize bespoke designs such as memory networks or custom transformers, which are specifically engineered to handle the storage and embedding-based fusion of personal information for effective role-playing Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)); [Jiang et al. ](https://arxiv.org/html/2407.11484v4#bib.bib42); Kottur et al. ([2017](https://arxiv.org/html/2407.11484v4#bib.bib44)); Li et al. ([2016](https://arxiv.org/html/2407.11484v4#bib.bib49)). These architectures provided highly specialized solutions that were adept within specific contexts but lacked the generalizability and scalability offered by later developments. The shift to PLMs like BERT mark a substantial enhancement in foundational capabilities Kenton and Toutanova ([2019](https://arxiv.org/html/2407.11484v4#bib.bib43)); Raffel et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib73)); Radford et al. ([2019](https://arxiv.org/html/2407.11484v4#bib.bib70)). These models leverage extensive pre-trained data, enhancing their ability to understand context and generate text, yet they still face limitations in fully grasping role-specific nuances. To overcome these challenges, researchers deploy innovative strategies like contrastive learning Huang et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib38)), bert-over-bert decouples learning Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)) and attention-based fusion mechanisms Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)) improve the integration of personal and dialogue data, enhancing role-playing functionalities.

##### LLM.

The current frontier in role-playing development is characterized by LLMs such as GPT-4, which boast an unprecedented scale in parameters and pre-training. Such LLMs offer profound improvements in understanding and generating text, capable of maintaining coherent and contextually rich personal dialogues even with minimal prompting. The architecture of these models has largely standardized around the decoder-only framework. Most LLM-based works Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)); Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)) customize various characters by configuring their personal background information in prompts, aiming at mimicking the specific role. Currently, several role-playing specific LLMs are developed to facilitate future research through instruction-tuning, such as CharacterGLM Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Xingye121212https://www.xingyeai.com/, Xingchen131313https://tongyi.aliyun.com/, Index Ind ([2024](https://arxiv.org/html/2407.11484v4#bib.bib1)), and Baichuan-Character141414https://npc.baichuan-ai.com/index.

Taking CharacterGLM as an example, let’s explore how LLMs can be optimized for role-playing support: The process begins with collecting character-related training corpus, where detailed character profiles are developed and utilized to engage in dialogues through either human interactions or LLMs, creating a rich dataset that captures the nuances of character-specific conversations. Following data collection, the next phase is instruction tuning, where the character profiles and accumulated dialogue data are organized into structured instructions. This stage also could incorporate the use of diverse prompts for data augmentation, enhancing the model’s ability to generate varied and contextually appropriate responses. The final but optional step, employs self-alignment—using outputs from advanced models for further training—and human feedback to refine and ensure character consistency. This comprehensive approach ensures that LLMs effectively embody and maintain character traits in role-playing scenarios.

Each stage builds upon the previous, with advancements addressing the limitations of earlier models and opening new possibilities for more complex and engaging role-playing interactions.

Beyond the methodologies outlined in public papers, we wish to underscore the influence of pre-training corpora on role-play tasks. From our pre-training experience, a corpus beneficial for training a generic assistant like ChatGPT may not necessarily aid a role-playing task, and the converse is also true. This is understandable, as a generic assistant requires data abundant in ‘real’ world knowledge (such as news, wiki) or data necessitating complex reasoning (like math, code). Novels, particularly those with a worldview divergent from reality, can induce serious knowledge hallucination issues. Role-playing often presupposes a scenario distinct from the real world, where a certain degree of reasonable knowledge hallucination is encouraged. Therefore, to our knowledge, a crucial step in pre-training an effective foundation model for role-playing involves incorporating a substantial amount of novels into the pretraining corpus, especially those with a worldview distinct from reality.

###  4.2 Alignment

Role-playing hinges on the precise alignment of language models with distinctive character-related information. In other words, alignment plays a crucial role in defining the upper limits of a model’s role-playing ability. Current methodologies for aligning language models with different roles fall into two broad categories: parameter-tuning alignment and parameter-frozen alignment.

####  4.2.1 Parameter-Tuning Alignment

Parameter-Tuning involves adjusting the model’s parameters to learn character-specific knowledge. In this manner, common approaches include:

  * •

Continue-Pretrain: Help models obtain character-related knowledge, addressing the domain gap between general pre-training and down-stream role-playing. This is essential because generic LLMs lack the nuanced understanding required to portray characters faithfully Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). ChatPlug Tian et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib96)) and MCP Huang et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib38)) train models on targeted literary corpora, capturing subtle narrative cues and character-specific lexicons essential for authentic character representation. To be more specific, ChatPlug continuly trains Qianwen series Bai et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib7)) on large-scale corpora including common documents and conversation corpus, pursuiting extensive open-world knowledge and foundation abilities, which are used for playing the role of celebrities in the following role-playing scenarios.

  * •

Supervised Fine-tuning (SFT): This is the most direct and conventional training approach, involving the concatenation of personal information and conversations for supervised learning. Various techniques Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)); Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)); Xu et al. ([2022a](https://arxiv.org/html/2407.11484v4#bib.bib115)) have been employed to enhance the learning of persona information during this phase. The core of these supervised methods lies in how to effectively model both role-related information and conversations simultaneously. Notable works include the use of attention routing mechanisms Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)) or memory networks Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)) to integrate both, and employing multiple structures to enhance the model’s understanding of both elements Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)); Song et al. ([2020a](https://arxiv.org/html/2407.11484v4#bib.bib91)). Currently, instruction tuning Wei et al. ([2022a](https://arxiv.org/html/2407.11484v4#bib.bib109)) has become the mainstream method for fine-tuning LLMs. During training, specific instructions and character-related data are provided, and the LLM learns through the next token prediction objectives. Typical works include RoleLLM Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), CharacterLLM Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)) and CharacterGLM Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)).

  * •

Self-Alignment: Self-alignment, which is regarded as a new approach that improves weaker LLMs by fine-tuning it on outputs from a stronger LLM. Inspired by this, CharacterGLM Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)) and Ditto Lu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib56)) employ self-generated data to further encourage LLMs to simulate role-play dialogues. Ditto proposes three steps for role-playing self-alignment: role knowledge collection, dialogue simulation and instruction tuning. It first collects role profiles from open-access knowledge bases such as Wikipedia and then simulates role-playing conversation corpus by conducting a reading comprehension task. At last, Ditto trains the models based on the self-generated datasets to enhance their role-playing abilities.

  * •

Parameter-Efficiency Fine-Tuning (PEFT): Given the vast parameters of current LLMs, training efficiency becomes a critical concern. Techniques like LoRA-tuning Hu et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib36)); Dettmers et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib23)) selectively train a subset of model parameters, which conserves computational resources. Han et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib35)) propose PersonaPKT, which represents each persona as condensing vectors to learn implicit persona-specific features. Such method only require less than 0.1% trainable parameters of the backbone while maintaining good response generation quality. Moreover, Yu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib124)) employ different LoRA modules to help LLMs imitate multiple characters simultaneously, balancing effectiveness with efficiency.

  * •

Reinforcement Learning: RLHF Ouyang et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib63)); Lambert and von Werra ([2022](https://arxiv.org/html/2407.11484v4#bib.bib45)) is a pivotal enhancement approach utilized predominantly after the SFT stage. In the context of role-playing, RLHF-related methods can also help LLMs to refine and align the generated responses more closely with the intended character traits and behaviors. Shea and Yu ([2023](https://arxiv.org/html/2407.11484v4#bib.bib84)) utilizes offline RL strategies to improve the persona consistency. Similarly, COMEDY Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)) utilizes GPT-4 to construct memory-based personal responses and memory-against personal responses, forming positive-negative pairs, and then employ DPO Rafailov et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib71)) strategies for aligning LLMs to generate more coherent memory-based personalized responses.

However, based on our practical engineering experience, we’ve found that 1) Reinforcement learning approaches, when using a reward model constructed through in-context learning, generally cannot exceed their inherent role-playing capabilities. 2) The task of annotating high-quality preference data for role-playing is significantly more challenging than for a generic assistant, as it necessitates a deep understanding of the specific character to accurately annotate preferences. For example, during the annotation of the HPD datasets Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), the authors enlisted the help of five avid Harry Potter fans to annotate Harry Potter’s behavior.




####  4.2.2 Parameter-Frozen Alignment

The parameter-frozen alignment approaches in role-playing offer a versatile framework to adapt to new roles without extensive retraining of the model’s parameters. These methods focus on utilizing existing model capabilities and enhancing them through strategic use of external data and contextual prompts.

  * •

In-Context Learning (ICL) Prompting: ICL-based approaches leverage the LLM’s inherent ability to contextualize and adapt its responses based on provided prompts and examples within the inputs Wei et al. ([2022c](https://arxiv.org/html/2407.11484v4#bib.bib111)); Brown et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib8)). To simulate the behavior of specific roles, ICL prompting is the simplest approach. Typically, filling with role attributes, relations, task requirements within ICL, current LLMs can adapt to different roles swiftly Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)); Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)). This method is highly effective for rapid deployment across varied characters Zhao et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib130)), making it ideal for scenarios where models need to switch roles or adapt to new narratives. Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)) even assign identities to multiple agents via ICL, make them simulating different roles in “WestWorld” sandbox game.

  * •

Retrieval Augmented Generation (RAG): RAG Shuster et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib89)); Li et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib48)) enhances role-playing by dynamically retrieving data from external databases before response generation. This method addresses the internal knowledge gaps of models about specific characters Liu et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib53)); Zhao et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib130)); Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)), reducing hallucinations—factually incorrect but plausible responses. By grounding responses in verifiable data, RAG enhances character consistency and enriches dialogues with precise, character-specific details.




####  4.2.3 Summary and Discussion.

Parameter-tuning alignment offers precise control over role-playing models behaviors, enabling deep customization and high specificity in character dialogue generation. However, they require significant computational resources, high-quality training data and risk overfitting, potentially reducing the model’s generalizability.

Conversely, parameter-frozen methods like In-context Learning and Retrieval Augmented Generation provide flexibility and scalability, allowing easy adaptation to new characters without extensive retraining. While reducing potential model biases, these methods depend heavily on abilities of LLMs and the quality and availability of external data, which can also introduce inaccuracies and result in hallucinations if not properly managed. Both approaches thus necessitate careful design to ensure the reliability and relevance of dialogue outputs.

##  5 Agent Architecture

{forest}

forked edges, for tree= grow=east, reversed=true, anchor=base west, parent anchor=east, child anchor=west, base=left, font=, rectangle, draw=hidden-draw, rounded corners, align=left, minimum width=4em, edge+=darkgray, line width=1pt, s sep=3pt, inner xsep=2pt, inner ysep=3pt, ver/.style=rotate=90, child anchor=north, parent anchor=south, anchor=center, , where level=1text width=4.0em,font=,, where level=2text width=7.5em,font=,, where level=3text width=7.2em,font=,, where level=4text width=6.4em,font=,, [ Agent Architecture, ver, color=orange!100, text=black [ Memory, color=orange!100, text=black [ Memory Sources, color=orange!100, text=black[ User-Agent Interaction, color=orange!100, text=black[ _e.g._ Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)), [Jiang et al. ](https://arxiv.org/html/2407.11484v4#bib.bib42), RoleInteract Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)), sys_leaf, text width=18em ] ] [Agent-Agent Interaction, color=orange!100, text=black[ _e.g._ Maas et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib58)), Generative Agent Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)), sys_leaf, text width=18em ] ] ] [ Memory Usage, color=orange!100, text=black[ Retrieval-based, color=orange!100, text=black[ _e.g._ PLATO-LTM Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)), Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)) , sys_leaf, text width=18em ] ] [Compressive-based, color=orange!100, text=black[ _e.g._ COMEDY Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), Wang et al. ([2023d](https://arxiv.org/html/2407.11484v4#bib.bib103)), sys_leaf, text width=18em ] ] ] ] [ Planning, color=orange!100, text=black[ Planning Formulation, color=orange!100, text=black [ _e.g._ Dasgupta et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib21)), Wang et al. ([2023d](https://arxiv.org/html/2407.11484v4#bib.bib103)), Inner Monologue Huang et al. ([2022a](https://arxiv.org/html/2407.11484v4#bib.bib37)), sys_leaf, text width=26.7em ] ] [ Planning Reflection, color=orange!100, text=black [ _e.g._ Generative Agent Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)), MORTISE Tang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib94)), Wu et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib113)), sys_leaf, text width=26.7em ] ] ] [ Action, color=orange!100, text=black[ _e.g._ RoleLLM Wang et al. ([2023e](https://arxiv.org/html/2407.11484v4#bib.bib106)), Humanoid Agents Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)), Shen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib85)), sys_leaf, text width=35.8em ] ] ]

Figure 5: The main content flow and categorization of Agent Architecture.  ![Refer to caption](x3.png) Figure 6: Typical system architecture of RPLAs. 

Building on the foundation of role-playing with large language models, Role-Playing Language Agents (RPLAs) take this concept further by incorporating interactive and autonomous behaviors Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)); Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)); Tang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib94)); Xi et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib114)). These agents not only embody specific characters but also engage in complex scenarios, making decisions and responding in ways that align with their designated roles. Such RPLAs are used to solve different challenging but interesting tasks not limited to original dialogue response generation. For instance, Chen et al. ([2024c](https://arxiv.org/html/2407.11484v4#bib.bib13)) present HOLLMWOOD, which design Writer, Editor and Actors RPLAs, simulating the human creative process for screenwriting tasks. The whole architecture of RPLAs often involves multiple modules that work in tandem. In this section, we introduce three important modules beyond the foundation response functionality in building well-performed RPLAs: memory, planning and action.

###  5.1 Memory

RPLAs often operate in environments that require them to remember and synthesize information over time, making memory modules Cao et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib10)) an essential component of their architecture. In the RPLA context, memory mainly consists of two source types:

  * •

User-Agent Interactions: This type is important in building user-centric RPLAs, which requires maintaining user related memories for generating consistent personalized responses in the ongoing interaction Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)); Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)); Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)). Specifically, within online role-playing dialogue platforms, the integration of long-term memory becomes crucial in sustaining user engagement and enhancing the immersive experience. In such environments, an RPLA equipped with long-term memory can significantly enrich the role-playing experience by adapting its responses based on the accumulated history of a user’s choices and interactions. Xu et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib118)) propose a long-term memory mechanism to extract and update long-term persona memories from use-bot interactions, enhancing the long-term personalized response consistency. Following this, Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)) further incorporate the memory operator to control the update or ignore of fine-grained memories.

  * •

Agent-Agent Interactions: In scenarios involving multiple RPLAs, this type of memory is essential for managing interactions between different agents. It supports scenarios where RPLAs must collaborate or compete within complex environments, such as multiplayer simulation games or interactive narratives. Memory in this context includes recording the outcomes of past interactions, thoughts and actions, which influence future strategies and decisions. This dynamic memory use allows RPLAs to adapt their behaviors based on previous experiences with other agents, fostering a more nuanced and strategic interaction framework that evolves over time. Generative Agents Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)) and Humanoid Agents Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)) create a virtual role-playing environment, where an RPLA can remember past alliances or conflicts with other agents, using this information to inform future decisions and interactions.




Given different memory forms from previous interactions, there are generally two approaches for integrating them in RPLA systems:

  * •

Retrieval-based: This approach is widely-used in current RPLAs, which involve maintaining a database that stores useful information from previous interactions Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)); Zhong et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib134)); Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)). Then facing the ongoing interaction, a retrieval module like sentence-embedding models Gao et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib31)); Chen et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib15)) fetches the most relevant information from this database based on the current context or recent interactions, helping the agent craft appropriate personalized responses or actions akin to the assigned character. Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)) retrieve relevant memories to help RPLAs make plans and decisions. Also, several memory management strategies like Memory Operator Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)) are used to ignore useless information and keep the memory base up-to-date. While retrieval-based memory offers clear benefits in enhancing RPLA interactions, it also poses challenges: 1) It heavily depends on the precision of the retrieval model; if these are not capable of accurately identifying relevant information, the coherence and appropriateness of responses can suffer; 2) It require substantial storage to maintain large corpus of past interactions, leading to increased storage costs and demanding efficient data management strategies to ensure quick and effective access to required information.

  * •

Compressive-based: This innovative approach addresses some limitations of retrieval-based memory by internalizing and condensing past information into a compact form, eliminating the need for extensive external databases. Compressive-based memory improves persona consistency by continuously updating and compressing historical data, which allows RPLAs to keep their responses current and relevant, like COMEDY Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)) and ReSummarize Wang et al. ([2023d](https://arxiv.org/html/2407.11484v4#bib.bib103)). COMEDY employ the “compress over compress” idea: it first summarizes each dialogue session into session-level memories, and then condenses them into a final compressive memory. Such method doesn’t rely on any sentence-embedding model as retriever and database. ReSummarize recursive summarizes the personalized memories from past sessions to keep the up-to-date memories. This method enhances the storage efficiency and reduces the dependency on large-scale data retrieval, although it sometimes sacrifice detail for compactness. 




###  5.2 Planning

In the realm of RPLAs, while foundational conversational capabilities are crucial, certain scenarios demand additional capacities such as advanced planning. Let LLMs simulate the human behavior in virtual environments could be a typical example, where strategic planning significantly enhances the role-playing experience Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)); Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)). Typically, planning in RPLAs comprises two stages: plan formulation and plan reflection.

  * •

Plan Formulation: In role-playing contexts, the formulation stage involves setting objectives that are consistent with the character’s motivations and the narrative’s demands Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)). Agents analyze the current scenario, predict possible future states, and devise a sequence of actions that will effectively portray their role. This might involve choosing to form alliances, solve puzzles, or navigate through challenges based on the storyline Wang et al. ([2023f](https://arxiv.org/html/2407.11484v4#bib.bib107)). Shen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib85)) prove that GPT4-level LLMs could make different plans according to their assigned MBTIs. Humanoid Agents Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)) further let RPLAs adapt their daily activities with other agents through planning.

  * •

Plan Reflection: After executing a plan, reflection allows RPLAs to assess their actions’ effectiveness in the context of the role-play. This might involve introspection on whether the chosen actions adequately advanced the narrative or stayed true to the character’s development. In simulations of human-like agents environments, Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)) and Wang et al. ([2023g](https://arxiv.org/html/2407.11484v4#bib.bib108)) utilize the feedback mechanisms to help RPLAs refine future planning. Take a simple example, if the agent’s actions in a simulated environment lead to unforeseen consequences, reflecting on these outcomes helps the agent adjust its strategy to better fit the narrative and user expectations in future scenarios.




By integrating such sophisticated planning and reflection capabilities, RPLAs can offer more dynamic and engaging experiences in role-play environments. These agents not only react to the unfolding story but actively contribute to its progression, making them integral players in shaping how narratives unfold.

###  5.3 Action 

Agent actions are the culmination of prior planning, memory utilization, and interactions. While the most recognizable form of action is generating responses aligned with role-play, actions within RPLAs extend beyond mere conversations. Tool use Schick et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib79)) as an another critical component of role-play actions, alongside a brief discussion on the potential for embodied actions in role-playing scenarios. For example, tools like search-related APIs Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)); Wang et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib101)); Zhao et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib130)) enable RPLAs to fetch and incorporate relevant character knowledge into their responses, enriching the dialogue with contextually appropriate content. This is particularly vital in scenarios requiring deep domain knowledge or historical accuracy, where tools can provide the necessary data to support the agent’s role accurately.

An emerging yet underexplored area in role-playing actions is embodied actions, where agents interact more tangibly within their environments. This involves physical interactions in virtual or augmented realities, representing a promising frontier for developing more immersive role-playing experiences. Although current role-playing applications primarily focus on dialogue and tool use, the potential for incorporating embodied actions offers exciting prospects for future advancements in RPLA interactivity and realism.

###  5.4 Summary and Discussion

In RPLAs development, the integration of memory, planning, and action modules is critical for delivering rich, interactive experiences. Current memory usage, both retrieval-based and compressive-based, offers strengths in contextual continuity and processing efficiency, respectively, yet faces challenges like high storage costs and potential loss of detail. Planning modules are essential for strategic decision-making within complex narratives but often struggle with transparency and robustness in dynamic scenarios. Action modules, encompassing dialogue and tool use, are well-developed for enhancing interactions, though the integration of tools needs careful management to avoid detracting from the user experience. Looking forward, advancements in these areas could include more nuanced memory retention, improved planning algorithms for better adaptability and transparency, and more integrated tool use, particularly with the potential expansion into embodied actions.

{forest} forked edges, for tree= grow=east, reversed=true, anchor=base west, parent anchor=east, child anchor=west, base=left, font=, rectangle, draw=hidden-draw, rounded corners, align=left, minimum width=4em, edge+=darkgray, line width=1pt, s sep=3pt, inner xsep=2pt, inner ysep=3pt, ver/.style=rotate=90, child anchor=north, parent anchor=south, anchor=center, , where level=1text width=4.0em,font=,, where level=2text width=6.5em,font=,, where level=3text width=7.2em,font=,, where level=4text width=6.4em,font=,, [ Evaluation, ver, color=carminepink!100, text=black [ Perspectives, color=carminepink!100, text=black [ Conversation Ability, color=carminepink!100, text=black[ _e.g._ Linguistic Quality Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)); Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)), Coherence Song et al. ([2020a](https://arxiv.org/html/2407.11484v4#bib.bib91)), eval_leaf, text width=27em ] ] [ Role-Persona   
Consistency, color=carminepink!100, text=black[ _e.g._ Attributes Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)), Relations Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)), eval_leaf, text width=27em ] ] [ Role-Behavior   
Consistency, color=carminepink!100, text=black[ _e.g._ Conversation Style Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)), Personality Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), eval_leaf, text width=27em ] ] [ Role-Playing   
Attractiveness, color=carminepink!100, text=black[ _e.g._ Human Likeness Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)), Engagement Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)),   
Proactivity Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)), Empathy Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)), eval_leaf, text width=27em ] ] ] [ Approaches, color=carminepink!100, text=black [ Reference-based, color=carminepink!100, text=black[ _e.g._ Absolute Metrics Dinan et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib25)), Multi-choice format Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)),   
Relative Metrics Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)), eval_leaf, text width=27em ] ] [ Human-based, color=carminepink!100, text=black[ _e.g._ Scoring-based Tian et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib96)), Ranking-based Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16), [2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), eval_leaf, text width=27em ] ] [ LLM-based, color=carminepink!100, text=black[ _e.g._ Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), Yu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib124)), eval_leaf, text width=27em] ] ] ]

Figure 7: The main content flow and categorization of Evaluation Section. 

##  6 Evaluation

Evaluating role-playing models is essential to ensure their effectiveness and realism in simulated environments. Unlike traditional chatbots or nlp tasks, role-playing require a more sophisticated set of evaluation dimensions and metrics due to their unique ability to emulate human-like interactions based on specific characters and contexts. In this section, we will first introduce various perspectives of role-playing evaluation, with a particular emphasis on its differences and connections with generic assistant evaluation. Following this, we will present several mainstream evaluation methods, primarily including reference-based, human-based, and LLM-based approaches

###  6.1 Evaluation Perspectives

The primary objective of role-playing with language models is to facilitate interactions that are not only contextually relevant but also tailored to reflect specific personas or roles. Thus, evaluating such models involves multiple dimensions that collectively assess how well these agents perform their intended roles. These dimensions include: conversation ability, role-persona consistency, role-behavior consistency and role-playing attractiveness.

####  6.1.1 Conversation Ability

In theory, the concept of role-playing should extend beyond just dialogue, but up until now, all role-playing studies have been confined to role-playing in conversations. Therefore, general conversation ability plays a crucial role in the role-playing experience. Generally, the conversation ability required in role-playing is similar to that of open-domain dialogue models, but there can be exceptions. For instance, if a user asks the model to act as a character who doesn’t speak, then this metric naturally becomes inapplicable.

  * •

Linguistic Quality: This emerges as a crucial perspective, encompassing both fluency and diversity Song et al. ([2020a](https://arxiv.org/html/2407.11484v4#bib.bib91)); Zheng et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib133)). Fluency assesses the grammatical correctness of responses, ensuring they are readable and free from obvious errors, while also considering the absence of repetitive structures and appropriate response length to effectively convey intended messages. Diversity, on the other hand, evaluates the richness of vocabulary used in responses. High lexical diversity signifies a model’s ability to vary its language use, which enriches dialogues, conveys nuanced information, and prevents conversations from becoming monotonous. Together, fluency and diversity in linguistic quality reflect the model’s capacity to produce engaging and contextually appropriate responses that are both clear and varied, enhancing user interaction and satisfaction.

  * •

Coherence: Coherence evaluates how relevant and logically consistent responses are with the ongoing conversation context Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)). It ensures that the dialogue flows naturally and that each response is contextually appropriate, maintaining continuity and relevance throughout the interaction. This involves the role-playing models’ ability to track dialogue history.




####  6.1.2 Role-Persona Consistency

Another fundamental ability of role-playing models is to consistently represent a specific character or persona throughout interactions. Role persona mainly include two perspectives:

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Attributes: Typically, attributes provide essential background information for language models to play as a specific role, significantly influencing the character’s reactions and interactions. Attributes define the persona of the agent, such as experiences, identities, interests, viewpoints, age, gender, achievements, and  titles. For instance, a dialogue agent playing the role of a doctor would have attributes consistent with medical knowledge, a compassionate demeanor, and an authoritative title Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)).

  * •

Relations: This involves the relationships between the speaker and others within the dialogue context. It considers how these relationships affect interaction dynamics, including familiarity, intimacy, animosity, or respect. For example, the relationship between Harry Potter and Ron is very different from the relationship between Harry and Voldemort Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)).




####  6.1.3 Role-Behavior Consistency

Building on the solid base provided by persona consistency, role behavior consistency involves more dynamic elements that adapt during interactions. To keep language models’ behavior consistent with a role requires a more sophisticated understanding of the context and ability to dynamically adjust, making the agent’s actions feel more natural and appropriate. It mainly includes:

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Conversational Style: The style of conversation should reflect the role’s typical manner of speech Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)). For example, a model playing a coach might use motivational and directive language, whereas one playing a friend might use a more casual and supportive style.

  * •

Personality: The portrayal of traits of the model should be consistent with its role. Consistency in personality helps in forming a solid, believable character, which enhances user engagement and contributes to a seamless narrative flow within the system. For instance, an agent embodying Harry Potter exhibits a patient and encouraging attitude to Ginny, while might display competitiveness toward Draco Malfoy Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)).

  * •

Linguistic Features: These include specific language use patterns, such as vocabulary, syntax, and register appropriate to the role. An elderly character might use more formal language and references from a different era, whereas a teenager might use slang and more relaxed grammar Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)).




####  6.1.4 Role-Playing Attractiveness

This is the most advanced level, where the role-playing models not only maintain persona and behavioral consistency but also enhances the interaction by being engaging, proactive, and empathetic:

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Human Likeness: The model should exhibit the naturalness of human interaction Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)). By interacting with what seem like real-world characters, users are more likely to engage deeply with the system.

  * •

Engagement: Moreover, role-playing models are expected to actively keep the user interested and involved in the conversation, adapting its strategies based on user engagement levels Chen et al. ([2024a](https://arxiv.org/html/2407.11484v4#bib.bib11)).

  * •

Proactivity: This is another key feature of role-playing language models, which needs to actively initiate and drive conversations Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)). It involves the agent taking initiative within the conversation, suggesting topics and actions before the user expresses specific needs. This proactive response capability allows role-playing models to anticipate user needs or interests and address them without explicit prompts from the user.

  * •

Empathy: It represents the peak of dialogue sophistication, where the agent recognizes emotional cues and responds in emotionally intelligent ways Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)); Zhang et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib128)). Whether offering encouragement, advice, or sympathy, the response is tailored to the user’s emotional state, thereby fostering a deeper emotional connection. This empathetic interaction is crucial for helping users address their emotional needs.




####  6.1.5 Summary and Discussion

Let’s recall the essence of role-playing, which is to interact with users in a manner consistent with a specific role. Therefore, the most crucial metric for role-playing should be the degree of similarity to the role being portrayed. Hence, according to the authors’ personal views and our experience in real-world applications, the more important dimensions among the above metrics should be Role-Persona consistency and Role-behavior consistency, as these two types of metrics truly measure the consistency of the LLM’s behavior with the role. Furthermore, if there is no specific demand for the model, we recommend using fewer but more comprehensive metrics, which can both reduce the difficulty of annotation and facilitate the training of a more general reward model.

Indeed, in our practical engineering applications, we evaluate a general role-playing model solely based on a single metric: model-role similarity, to measure the alignment degree between the model and the role in any scenarios. Furthermore, when considering launching this model in a specific region, we use safeness to evaluate whether the model complies with the laws and regulations of that region. When the model is used to play a game NPC, we evaluate whether the model’s answers contain knowledge hallucinations that are inconsistent with the game background.

###  6.2 Evaluation Approaches

To evaluate role-playing language models on the aforementioned dimensions, existing methods can be categorized into three main types: Reference-based, human-based, and LLM-based evaluation. The last type of evaluation method has emerged with the introduction of ChatGPT, and its principle is to use LLM to simulate human evaluation.

####  6.2.1 Reference-based Evaluation

The reference output (ground truth) in the test set represents what users expect to see from the model output, essentially reflecting the most authentic user needs. Therefore, based on the assumption that ‘the more similar the model’s output is to the reference, the better the model performs’, researchers have begun to use some reference-based metrics to evaluate the consistency between the model output and the test set. This is often associated with metrics like Perplexity (PPL) Zhang et al. ([2018](https://arxiv.org/html/2407.11484v4#bib.bib127)), BLUE Papineni et al. ([2002](https://arxiv.org/html/2407.11484v4#bib.bib65)), and ROUGE Lin ([2004](https://arxiv.org/html/2407.11484v4#bib.bib52)). PPL is used to estimate the likelihood of the reference output on the evaluated model, while BLEU and ROUGE are used to measure the similarity between the generated output and the reference. The key difference between the former and the latter two lies in the fact that PPL measures the alignment at the distribution level between the model and the reference, independent of the decoding algorithm. On the other hand, BLEU and ROUGE require the model to first decode a text output, and then evaluate the similarity between the two pieces of text.

Although we already have a variety of commonly used reference-based metrics, they still fall short in evaluating how well the responses align with personas or characters. To address this, several methods are explored the multi-choice answer format evaluation Salemi et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib76)); Shen et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib86)), where LLM need to choose the most persona-consistent response from given options rather than open-end generation, measuring accuracy directly.

An alternative approach emphasizes the use of relative automatic metrics, such as Delta PPL, to gauge the model’s role-playing capability using triplets like (x,y\text⁢w⁢i⁢n,y\text⁢l⁢o⁢s⁢e)𝑥subscript𝑦\text𝑤𝑖𝑛subscript𝑦\text𝑙𝑜𝑠𝑒(x,y_{\text{win}},y_{\text{lose}})( italic_x , italic_y start_POSTSUBSCRIPT italic_w italic_i italic_n end_POSTSUBSCRIPT , italic_y start_POSTSUBSCRIPT italic_l italic_o italic_s italic_e end_POSTSUBSCRIPT ). Specifically, it considers the difference in Perplexity (PPL) Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)) between positive and negative instances in the test set as a measure of the model’s role-playing proficiency.

To the best of our knowledge and experience, Delta PPL is the sole reference-based model capable of objectively and accurately assessing an LLM’s role-playing ability. However, this metric is heavily dependent on a meticulously curated test set comprising data in triplet form.

####  6.2.2 Human-based Evaluation

This is critically important in assessing role-playing models because automatic metrics often fail to capture the nuanced aspects of responses that are essential for realistic and engaging character simulation. Therefore, human-based evaluation is considered one of the most effective methods for thoroughly assessing performance.

To the best of our knowledge, within the realm of industrial applications, human evaluation stands as the most crucial, if not the only reliable, method of evaluation. This is primarily because only humans can authentically replicate the experiences of other human users, thereby offering insights into the actual user experience.

Theoretically, all the perspectives introduced in the previous section can be evaluated by human annotators, but generally, due to cost and the limitations of annotators’ capabilities, it is recommended to integrate multiple dimensions into a single dimension for evaluation. In terms of implementation, human-based evaluations typically consist of two approaches:

(1) Scoring: In this method, each relevant dimension is assigned a specific scoring threshold, and annotators rate the model’s responses based on these criteria Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)); Tu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib99)). Common manual scoring metrics for evaluating role-playing models encompass several key aspects: Consistency, which ensures that responses align with the attributes and behaviors specified in the character’s profile; Human-likeness, assessing how well the responses mimic human characteristics and natural communication; and Engagement, measuring the ability of the response to capture attention or arouse curiosity. Additionally, metrics like coherence and fluency are typically included to evaluate how well responses fit into the overall dialogue context and their linguistic smoothness, respectively. To aid in this evaluation process, it is also common practice to provide human annotators with examples for each criterion, helping them to better understand and apply the evaluation standards consistently and effectively. This structured approach ensures a comprehensive assessment of the role-playing model’s performance across multiple dimensions of dialogue quality. But it is also complex due to the need for detailed scoring samples for each rating. It also adds to the complexity of the evaluation process and might not facilitate easy comparisons across multiple models.

(2) Ranking: Another alternative approach is to present responses from multiple models to annotators who then rank these in order of quality or categorize them into win/tie/lose outcomes Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16), [2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)). A typical example of this method in use is with instructGPT Ouyang et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib63)), where human labelers rank the model’s outputs from best to worst based on quality. Similarly, Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)) employ annotators to rank various models in terms of their role-playing capabilities, specifically evaluating them based on their relevance with scenes, characters attributes, and relations. This ranking approach allows for a direct comparison of models, giving a clearer picture of their respective strengths and weaknesses in simulating realistic and contextually appropriate role-playing interactions. Ranking is more efficient and can compare multiple models simultaneously, but it generally provides less granularity than scoring because it aggregates the quality into broader categories.

Despite our earlier emphasis that “human evaluation is the only reliable method of evaluation”, its practical application is often beset with challenges due to numerous inherent shortcomings. The most critical issue stems from the subjectivity of human annotators. This subjectivity can result in: 1) Significant bias, particularly when the annotation process lacks rigor. A typical scenario we frequently encounter involves model developers or paper authors inviting their colleagues or friends to act as annotators. In such cases, these individuals are naturally inclined to award higher scores to ‘the proposed method.’ 2) The inability to directly reuse the human-evaluation results from one paper in another, thereby compelling each paper to invest substantial time and resources in conducting human evaluations for all baselines.

####  6.2.3 LLM-based Evaluation

In light of the aforementioned issues with human evaluation, some researchers have started experimenting with LLMs as annotators for role-playing tasks Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)); Yu et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib124)). Undoubtedly, employing LLMs as annotators can mitigate the issues of bias and cost to some extent. However, this approach gives rise to a new question: Are LLMs sufficiently competent as annotators for role-playing tasks? Wang et al. ([2023c](https://arxiv.org/html/2407.11484v4#bib.bib102))

In practice, LLM-based evaluation involves detailed instructions that include evaluation dimensions and thresholds, which generally align with the criteria used in human assessments Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16), [2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)). To guide the LLM towards more accurate evaluations, several related scoring examples are often provided and formulated in a predefined format that facilitates straightforward aggregation and analysis of the results. In general, LLM-based evaluations reduce the need for extensive human annotator training and coordination, providing a scalable option for accessing models effectiveness. Despite the speed and reduced logistical overhead, LLM-based evaluations often face challenges in achieving consistency with human judgments. When LLMs are tasked with evaluating a role they are not familiar with, the accuracy of the evaluation may be compromised. There are also several general shortcomings, such as LLMs’ sensitivity to order when scoring, often giving a higher rank to responses placed earlier. Furthermore, LLMs tend to assign higher ranks to lengthier responses. Lastly, LLMs typically struggle to accurately evaluate models that possess superior role-playing capabilities than their own. For example, a reward model based on ChatGPT would not be able to accurately assess the capabilities of a role-playing model based on GPT-4. In such scenarios, if the goal is to train a role-playing model that surpasses the state-of-the-art performance, the use of LLM-based evaluation becomes unsuitable.

In some recent studies, we have observed a concerning trend: researchers are increasingly relying on LLM-based evaluation without adequately verifying the effectiveness of this metric for their specific tasks. In fact, we have meticulously replicated some of the methods used for evaluation in certain papers, and regrettably found that the effectiveness of these methods is actually far behind that of human experts151515To prevent any potential negative implications for the authors of these papers, we refrain from citing them here, but they are all included in the references of this paper. Such low-quality evaluations can trigger a series of chain reactions, such as the follow-up work of these studies continuing to use these evaluation methods. Consequently, we make this appeal: authors must provide ample evidence within their papers to substantiate that their LLM-based evaluation method is an appropriate evaluator for their specific scenarios. For example, they could select a small sampled set and illustrate the consistency between well-instructed human annotators and LLM evaluators on these samples. As contributors of knowledge to the community, it is essential for the authors to ensure the accuracy and validity of this knowledge.

####  6.2.4 Evaluating RPLAs

Evaluating RPLAs, especially in scenarios involving the collaboration of multiple agents such as MetaAgents Li et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib51)) or GenerativeAgents Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)), presents unique challenges. In these settings, the effectiveness of these models in role-playing is assessed based on their ability to achieve predefined goals or complete specific tasks. The evaluation process typically focuses on whether the agents can successfully fulfill the roles and objectives they are assigned, which is crucial in settings where multiple agents must work together coherently. Metrics such as accuracy are commonly used to quantitatively measure how well these agents meet their goals. Additionally, qualitative evaluations might involve human judges assessing the appropriateness and effectiveness of specific actions taken by the agents within the scenario. For instance, in the simulation of human behaviors, the evaluation of RPLAs often involves comparing their actions to those of human-annotated standards to determine their accuracy and effectiveness.

####  6.2.5 Summary and Discussion

Evaluating role-playing language models effectively often involves a mix of methods, each with its own strengths and weaknesses. Reference-based metrics are efficient and objective, providing quick, quantifiable results ideal for preliminary assessments, though they lack depth and context sensitivity, failing to capture nuances like persona consistency. Human-based evaluations offer deep insights into nuances and subtleties in dialogues, including character alignment and user engagement, but are costly and less scalable, with potential for subjective variability between evaluators. LLM-based evaluations, leveraging the capabilities of large language models, offer scalability and speed and can mimic some aspects of human judgment, yet they may not always align with human evaluations and depend heavily on the used LLMs.

Finally, we want to emphasize a principle that is the most fundamental for role-playing evaluation: regardless of the evaluation method adopted, one must meticulously verify that the method is capable of evaluating cases in their specific scenarios. To illustrate, consider these simple examples: when carrying out human & LLM-based evaluations, we must ensure that the annotators & LLMs possess sufficient background knowledge of the roles to be evaluated; otherwise, they lack the necessary capability to evaluate this task. On the other hand, when using the ΔΔ\Deltaroman_Δ PPL metric for evaluation, we must also ensure that the quality of the positive response is indeed higher than that of the negative response.

##  7 Challenges and Future Directions

Building role-playing language models face a myriad of challenges that impact their development and effectiveness in delivering complex, interactive narratives. While these models or agents have shown promising capabilities, significant hurdles remain in metrics development, evaluation accuracy, and system adaptability. This section explores these challenges in detail, identifying key areas where current systems fall short and proposing directions for future research. Please note that we have already introduced some challenges earlier in the text. In this section, we will bring all these elements together for a comprehensive summary.

##### More Reference-based Metrics for evaluating Role-Playing.

Current reference-based evaluation metrics for role-playing primarily focus on linguistic accuracy and coherence, which are insufficient for assessing role-playing capabilities that require character consistency and narrative engagement Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). To the best of our knowledge and experience, ΔΔ\Deltaroman_Δ PPL Song et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib90)) is the sole reference-based model capable of objectively and accurately assessing an LLM’s role-playing ability. However, methods based on PPL are only capable of evaluating effects at the distribution level, and they fall short when it comes to assessing the quality of the final generated results, which are derived from decoding the output distribution. To our knowledge, as of now, there is no existing method that can directly evaluate the consistency between the assigned role and the generated output.

##### Sensitivity in LLM-Based Evaluation.

LLM-based evaluations often face challenges in achieving consistency with human judgments. When LLMs are tasked with evaluating a role they are not familiar with, the accuracy of the evaluation may be compromised. There are also several general shortcomings, such as LLMs’ sensitivity to order when scoring, often giving a higher rank to responses placed earlier. Furthermore, LLMs tend to assign higher ranks to lengthier responses. Lastly, LLMs typically struggle to accurately evaluate models that possess superior role-playing capabilities than their own. For example, a reward model based on ChatGPT would not be able to accurately assess the capabilities of a role-playing model based on GPT-4. In such scenarios, if the goal is to train a role-playing model that surpasses the state-of-the-art performance, the use of LLM-based evaluation becomes unsuitable.

##### Imbalance, Bias and Cost in Human-based Evaluation.

While human evaluation is invaluable for capturing the nuance and complexity of role-play interactions, it is resource-intensive and difficult to standardize. First, annotators could exhibit bias in their evaluation, such as position biases, the preference for verbose and complex responses Pandey et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib64)); Santurkar et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib77)). On the other hand, training evaluators to consistently assess role-playing performance is challenging, particularly when dealing with subjective interpretations of character and narrative Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). For instance, validating the models’ performances of playing Harry Potter requires that human evaluators be familiar with the magic worlds and characters relationships with Harry in different storylines. Moreover, balancing and calibrating human judgments to ensure reliability across diverse scenarios adds another layer of complexity Ethayarajh and Jurafsky ([2022](https://arxiv.org/html/2407.11484v4#bib.bib28)). Developing more structured evaluation frameworks and training protocols could help mitigate these issues Prassl and Risak ([2017](https://arxiv.org/html/2407.11484v4#bib.bib67)); Clark et al. ([2021](https://arxiv.org/html/2407.11484v4#bib.bib19)).

##### Lack of deeper Role-specific Alignment Approaches. 

Aligning language models with specific roles, particularly using LLMs, presents significant challenges. Current approaches primarily integrate persona and interaction context directly into input prompts or instructions Tu et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib98)); Zhou et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib135)). However, these methods often lack depth in modeling the complexities of character roles, such as relationships between characters, their psychological states, or evolving dynamics throughout an interaction Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)). This surface-level adaptation does not allow models to learn and adapt to the nuanced interactions that define characters within narratives, limiting the models’ ability to deliver immersive and contextually rich interactions. Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)) prove that understanding and replicating how a character’s emotional response might evolve in reaction to another character’s actions or environmental changes are areas that are seldom explored in depth. Future research needs to focus on developing techniques that enable role-playing language models to internalize and dynamically represent interpersonal relationships and psychological states of characters. This could involve sophisticated training regimes that incorporate dynamic character-based scenarios, psychological profiling, and relationship mapping. Such advancements would enhance the realism and engagement in role-playing, making them more effective for applications in gaming, training simulations, and interactive storytelling, where accurate and dynamic role portrayal is crucial for enhancing user experience.

##### Ensure the safety in Role-Playing.

Ensuring safety in role-playing language models involves addressing several key areas: privacy Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), toxicity Wen et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib112)); Gehman et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib32)); Deshpande et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib22)), and biased or discriminatory behavior Nangia et al. ([2020](https://arxiv.org/html/2407.11484v4#bib.bib60)); Rutinowski et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib74)); Shaikh et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib81)). Privacy concerns are paramount, especially on platforms where users interact extensively with agents. These agents often process and store sensitive personal information derived from interactions to enhance the role-playing experience. Ensuring that this information is not misused or inadvertently leaked is critical. Techniques such as data anonymization Eurojust and Europol ([2019](https://arxiv.org/html/2407.11484v4#bib.bib29)), secure data storage, and strict access controls are necessary to protect user privacy. Additionally, implementing protocols that ensure data is only used to enhance the interaction, without being stored longer than necessary, can help maintain user trust.

Toxic role-related content often stems from the data used during the pre-training and fine-tuning phases, which may include biased or harmful language from the internet or other sources Rutinowski et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib74)). This is particularly problematic in role-playing scenarios, where agents are expected to adapt to diverse personas that may inadvertently include or trigger negative content. To counter this, it’s essential to employ robust content moderation systems and retrain models using curated, non-toxic datasets. Continuous monitoring and updating of the model’s responses using feedback loops can also help minimize the emergence of undesirable content.

Moreover, role-playing language models can exhibit biased or discriminatory behavior if their training data contains implicit cultural or societal biases Brown-Cohen et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib9)). Ma et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib57)) further conduct Jailbreak Attack on MultiModal LLMs via Role-playing Image Character. Such phenomenon is especially concerning in role-playing, where such behavior can significantly detract from the user experience and perpetuate harmful stereotypes. To address this, diversity and inclusion must be integral to the training process. Techniques like fairness audits, bias testing, and incorporating diverse datasets in training can help reduce these issues. Furthermore, regular updates and checks on the model’s outputs against established fairness standards can ensure more balanced interactions.

##### Hallucination in Role-Playing.

Hallucination in LLMs, particularly pronounced in knowledge-intensive tasks, poses significant challenges for LLMs to generate knowledge-consistent responses Ji et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib40)); Xu et al. ([2024b](https://arxiv.org/html/2407.11484v4#bib.bib118)). This issue, known as character hallucination Shao et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib83)); Ahn et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib3)); Sadeq et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib75)), occurs when the language models generate responses that are inconsistent with the defined profiles or historical context of a character. For example, a model designed to role-play Queen Elizabeth I using modern slang or discussing contemporary issues would be a clear instance of character hallucination due to its anachronistic and contextually inaccurate content. A more complex aspect, point-in-time character hallucination Chen et al. ([2023b](https://arxiv.org/html/2407.11484v4#bib.bib16)), involves maintaining narrative consistency over time, such as ensuring a character’s responses evolve correctly according to their development in the storyline.

To mitigate these issues, effective strategies include fine-tuning within character-related domain knowledge Tian et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib96)), where models are specifically adjusted to reflect accurate character and narrative details, and RAG, which supplements the model’s responses with real-time, role-specific information. These approaches enhance the model’s ability to generate temporally and contextually appropriate responses, thereby reducing inaccuracies and improving the overall reliability and immersion of language models in role-playing scenarios. Addressing such issues is crucial for developing sophisticated role-playing agents that accurately embody characters and maintain narrative fidelity throughout interactions.

##### Memory Usage in RPLAs.

Memory module in RPLAs is essential for providing contextual continuity, role-playing interactions, and deep narrative engagement. However, the implementation faces significant challenges, particularly in managing large volumes of data Wang et al. ([2023d](https://arxiv.org/html/2407.11484v4#bib.bib103)), ensuring the accuracy of compressed memories Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), efficiently updating memory databases Bae et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib6)), and achieving precise memory retrieval Xu et al. ([2022b](https://arxiv.org/html/2407.11484v4#bib.bib117)). One primary challenge is handling the extensive input context length, which can overwhelm the model if all past interactions are directly inputted as memory. To manage this, RPLAs often use compressed versions of past interactions Wang et al. ([2023d](https://arxiv.org/html/2407.11484v4#bib.bib103)); Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)), which must be carefully crafted to ensure no critical information is lost. Furthermore, the operation of memory databases requires sophisticated algorithms for timely updates and appropriate forgetting of irrelevant data to maintain efficiency and relevance. Retrieval accuracy also poses a major challenge, especially in long-term real-world scenarios where the precision of fetched memories significantly impacts the quality of interactions. To address these issues, researchers have proposed various innovative solutions. Zhong et al. ([2024](https://arxiv.org/html/2407.11484v4#bib.bib134)) suggest using a memory bank approach where memory updating is governed by a forgetting curve, which helps in phasing out outdated or less useful memories over time. Others advocate for multiple compressions of memory to reduce dependency on retrieval models Chen et al. ([2024d](https://arxiv.org/html/2407.11484v4#bib.bib14)), thereby minimizing the risks of retrieving inaccurate memories.

Despite these efforts, memory management in RPLAs remains a complex, ongoing issue that requires continued research and development. Future advancements will need to focus on refining these strategies, ensuring that RPLAs can effectively recall and utilize memories to enhance user interaction without compromising system performance.

##### Multi-Modal Integration in RPLAs.

While the majority of RPLAs traditionally operate with text-based inputs and outputs, incorporating multi-modal interactions can significantly enhance the depth and realism of role-playing. Integrating image-text pairs allows models to learn more detailed character traits, as demonstrated in Ahn et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib4)). Similarly, Zhao et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib130)) leverages models like stable diffusion to generate enriched environment in narrative role-playing where textual information is insufficient, adding depth to the storytelling. The integration of multi-modal interactions represents a promising frontier for enhancing the capabilities and effectiveness of RPLAs. As progresses, the potential to seamlessly blend visual, auditory, and textual data will likely expand, opening up new avenues for creating more immersive and realistic role-playing experiences.

##### Make RPLAs be lifelong learner.

Lifelong learning represents a pivotal long-term goal for RPLAs, aiming to enable these systems to continuously adapt and evolve in response to user interactions and environmental changes Park et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib66)); Wang et al. ([2023a](https://arxiv.org/html/2407.11484v4#bib.bib100)). This capability is crucial for maintaining the relevance and effectiveness of RPLAs over extended periods.

However, the potential for lifelong learning introduces significant challenges, particularly in maintaining alignment and safety. As RPLAs evolve, they may develop capabilities that lead to unintended or harmful outcomes, such as reward hacking or misaligned goals, which could manipulate monitoring systems or pursue unethical strategies Shevlane ([2022](https://arxiv.org/html/2407.11484v4#bib.bib87)); Shevlane et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib88)). Ensuring that RPLAs remain aligned with ethical standards and their intended roles requires continuous oversight and adaptive mechanisms to correct and guide their learning trajectories Brown-Cohen et al. ([2023](https://arxiv.org/html/2407.11484v4#bib.bib9)); Cohen et al. ([2022](https://arxiv.org/html/2407.11484v4#bib.bib20)). Developing robust frameworks to manage lifelong learning—incorporating advanced monitoring and adjustment techniques—will be crucial for the successful integration of RPLAs into various domains, from entertainment to customer service, ensuring they remain beneficial and safe as they evolve.

###  7.1 Summary and Discussion

In general, the advancement of role-playing language models faces numerous challenges, including the development of specific evaluation metrics, efficient memory management, ensuring role alignment, maintaining safety, and facilitating lifelong learning. Each of these areas presents unique obstacles that impact the effectiveness and safety in dynamic environments. Addressing these issues requires an integrated approach that combines technological innovation with strict adherence to ethical standards, ensuring the model not only perform their roles with enhanced accuracy and realism but also operate safely and responsibly. Future progress in this field will depend on our ability to balance these complex factors, paving the way for role-playing language models to transform interactive storytelling and digital interactions across various applications.

##  8 Conclusion

The development of role-playing with language models represents a significant evolution in the field of artificial intelligence. From their inception, where models primarily focused on maintaining simple persona consistencies, to the present day, where advanced LLMs facilitate complex and nuanced character-driven interactions, the progress has been substantial. This survey highlights key advancements in data sourcing, foundation models, alignment, system architecture, and evaluation, demonstrating that current models not only maintain character consistency but also exhibit dynamic behavior and attractiveness. Despite the progress, challenges such as dynamic persona management, behavioral alignment, ethical considerations, and the development of comprehensive evaluation metrics persist. Addressing these challenges will be crucial for future advancements, aiming to create more immersive and realistic role-playing applications with LLMs.

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url: http://arxiv.org/abs/2502.16940v1
title: [2502.16940v1] Reasoning Does Not Necessarily Improve Role-Playing Ability
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# Computer Science > Computation and Language

**arXiv:2502.16940v1** (cs) 

[Submitted on 24 Feb 2025]

# Title:Reasoning Does Not Necessarily Improve Role-Playing Ability

Authors:[Xiachong Feng](https://arxiv.org/search/cs?searchtype=author&query=Feng,+X), [Longxu Dou](https://arxiv.org/search/cs?searchtype=author&query=Dou,+L), [Lingpeng Kong](https://arxiv.org/search/cs?searchtype=author&query=Kong,+L)

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> Abstract:The application of role-playing large language models (LLMs) is rapidly expanding in both academic and commercial domains, driving an increasing demand for high-precision role-playing models. Simultaneously, the rapid advancement of reasoning techniques has continuously pushed the performance boundaries of LLMs. This intersection of practical role-playing demands and evolving reasoning capabilities raises an important research question: "Can reasoning techniques enhance the role-playing capabilities of LLMs?" To address this, we conduct a comprehensive study using 6 role-playing benchmarks, 24 LLMs, and 3 distinct role-playing strategies, comparing the effectiveness of direct zero-shot role-playing, role-playing with Chain-of-Thought (CoT), and role-playing using reasoning-optimized LLMs. Our findings reveal that CoT may reduce role-playing performance, reasoning-optimized LLMs are unsuitable for role-playing, reasoning ability disrupts the role-playing scaling law, large models still lack proficiency in advanced role-playing, and Chinese role-playing performance surpasses English role-playing performance. Furthermore, based on extensive experimental results, we propose two promising future research directions: Role-aware CoT for improving role-playing LLMs and Reinforcement Learning for role-playing LLMs, aiming to enhance the adaptability, consistency, and effectiveness of role-playing LLMs for both research and real-world applications. 

Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
---|---  
Cite as: | [arXiv:2502.16940](https://arxiv.org/abs/2502.16940) [cs.CL]  
  | (or  [arXiv:2502.16940v1](https://arxiv.org/abs/2502.16940v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2502.16940> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Xiachong Feng [[view email](/show-email/404780ef/2502.16940)]   
**[v1]** Mon, 24 Feb 2025 08:08:41 UTC (196 KB)  


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url: http://arxiv.org/abs/2501.14371v1
title: [2501.14371v1] DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
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# Computer Science > Computation and Language

**arXiv:2501.14371v1** (cs) 

[Submitted on 24 Jan 2025]

# Title:DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing

Authors:[Xinyu Ma](https://arxiv.org/search/cs?searchtype=author&query=Ma,+X), [Yifeng Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+Y), [Yang Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+Y), [Tianlong Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+T), [Xu Chu](https://arxiv.org/search/cs?searchtype=author&query=Chu,+X), [Xin Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+X), [Junfeng Zhao](https://arxiv.org/search/cs?searchtype=author&query=Zhao,+J), [Yasha Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y)

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> Abstract:We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model's representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Codes and benchmark datasets are available at [this https URL](https://github.com/ArthurLeoM/DRESS-LLM). 

Comments: | ICLR 2025 Accepted  
---|---  
Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)  
Cite as: | [arXiv:2501.14371](https://arxiv.org/abs/2501.14371) [cs.CL]  
  | (or  [arXiv:2501.14371v1](https://arxiv.org/abs/2501.14371v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2501.14371> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Xinyu Ma [[view email](/show-email/7fb09511/2501.14371)]   
**[v1]** Fri, 24 Jan 2025 10:04:53 UTC (1,085 KB)  


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url: http://arxiv.org/abs/2404.19721v3
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[2404.19721v3] PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games

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# Computer Science > Artificial Intelligence

**arXiv:2404.19721v3** (cs) 

[Submitted on 30 Apr 2024 ([v1](https://arxiv.org/abs/2404.19721v1)), last revised 9 Jul 2024 (this version, v3)]

# Title:PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games

Authors:[Steph Buongiorno](https://arxiv.org/search/cs?searchtype=author&query=Buongiorno,+S), [Lawrence Jake Klinkert](https://arxiv.org/search/cs?searchtype=author&query=Klinkert,+L+J), [Tanishq Chawla](https://arxiv.org/search/cs?searchtype=author&query=Chawla,+T), [Zixin Zhuang](https://arxiv.org/search/cs?searchtype=author&query=Zhuang,+Z), [Corey Clark](https://arxiv.org/search/cs?searchtype=author&query=Clark,+C)

View a PDF of the paper titled PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games, by Steph Buongiorno and 4 other authors

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> Abstract:This research introduces Procedural Artificial Narrative using Generative AI (PANGeA), a structured approach for leveraging large language models (LLMs), guided by a game designer's high-level criteria, to generate narrative content for turn-based role-playing video games (RPGs). Distinct from prior applications of LLMs used for video game design, PANGeA innovates by not only generating game level data (which includes, but is not limited to, setting, key items, and non-playable characters (NPCs)), but by also fostering dynamic, free-form interactions between the player and the environment that align with the procedural game narrative. The NPCs generated by PANGeA are personality-biased and express traits from the Big 5 Personality Model in their generated responses. PANGeA addresses challenges behind ingesting free-form text input, which can prompt LLM responses beyond the scope of the game narrative. A novel validation system that uses the LLM's intelligence evaluates text input and aligns generated responses with the unfolding narrative. Making these interactions possible, PANGeA is supported by a server that hosts a custom memory system that supplies context for augmenting generated responses thus aligning them with the procedural narrative. For its broad application, the server has a REST interface enabling any game engine to integrate directly with PANGeA, as well as an LLM interface adaptable with local or private LLMs. PANGeA's ability to foster dynamic narrative generation by aligning responses with the procedural narrative is demonstrated through an empirical study and ablation test of two versions of a demo game. These are, a custom, browser-based GPT and a Unity demo. As the results show, PANGeA holds potential to assist game designers in using LLMs to generate narrative-consistent content even when provided varied and unpredictable, free-form text input. 

Subjects: |  Artificial Intelligence (cs.AI); Computation and Language (cs.CL)  
---|---  
Cite as: | [arXiv:2404.19721](https://arxiv.org/abs/2404.19721) [cs.AI]  
  | (or  [arXiv:2404.19721v3](https://arxiv.org/abs/2404.19721v3) [cs.AI] for this version)   
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## Submission history

From: Steph Buongiorno [[view email](/show-email/8ce3b5a9/2404.19721)]   
**[[v1]](/abs/2404.19721v1)** Tue, 30 Apr 2024 17:11:54 UTC (995 KB)  
**[[v2]](/abs/2404.19721v2)** Sun, 30 Jun 2024 20:54:57 UTC (1,350 KB)  
**[v3]** Tue, 9 Jul 2024 23:45:27 UTC (1,396 KB)  


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url: http://arxiv.org/abs/2202.12213v2
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[2202.12213v2] Geometric decomposition of geodesics and null phase curves using Majorana star representation

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# Quantum Physics

**arXiv:2202.12213v2** (quant-ph) 

[Submitted on 24 Feb 2022 ([v1](https://arxiv.org/abs/2202.12213v1)), last revised 7 Jun 2022 (this version, v2)]

# Title:Geometric decomposition of geodesics and null phase curves using Majorana star representation

Authors:[Vikash Mittal](https://arxiv.org/search/quant-ph?searchtype=author&query=Mittal,+V), [Akhilesh K.S.](https://arxiv.org/search/quant-ph?searchtype=author&query=K.S.,+A), [Sandeep K. Goyal](https://arxiv.org/search/quant-ph?searchtype=author&query=Goyal,+S+K)

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> Abstract:Geodesics are the shortest curves between any two points on a given surface. Geodesics in the state space of quantum systems play an important role in the theory of geometric phases, as these are also the curves along which the acquired geometric phase is zero. Null phase curves (NPCs) are the generalization of the geodesics, which are defined as the curves along which the acquired geometric phase is zero even though they need not be the shortest curves between two points. Here we present a geometric decomposition of geodesics and NPCs in higher-dimensional state space, which allows understanding the intrinsic symmetries of these curves. We use Majorana star representation to decompose a geodesic in the $n$-dimensional Hilbert space to $n-1$ curves on the Bloch sphere and show that all the $n-1$ curves are circular segments with specific properties that are determined by the inner product of the end states connected by the given geodesic. We also propose a method to construct infinitely many NPCs between any two arbitrary states for $(n>2)$-dimensional Hilbert space using our geometric decomposition. 

Comments: | 9 pages, 4 figures  
---|---  
Subjects: |  Quantum Physics (quant-ph)  
Cite as: | [arXiv:2202.12213](https://arxiv.org/abs/2202.12213) [quant-ph]  
  | (or  [arXiv:2202.12213v2](https://arxiv.org/abs/2202.12213v2) [quant-ph] for this version)   
  |  <https://doi.org/10.48550/arXiv.2202.12213> Focus to learn more arXiv-issued DOI via DataCite  
Journal reference: | Phys. Rev. A 105, 052219 (2022)  
Related DOI:  | <https://doi.org/10.1103/PhysRevA.105.052219> Focus to learn more DOI(s) linking to related resources   
  
## Submission history

From: Vikash Mittal [[view email](/show-email/7b4bf129/2202.12213)]   
**[[v1]](/abs/2202.12213v1)** Thu, 24 Feb 2022 17:21:17 UTC (452 KB)  
**[v2]** Tue, 7 Jun 2022 19:20:26 UTC (453 KB)  


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url: http://arxiv.org/abs/2307.07959v1
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[2307.07959v1] Role of Pore Dilation in Molecular Transport through the Nuclear Pore Complex: Insights from Polymer Scaling Theory

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# Physics > Biological Physics

**arXiv:2307.07959v1** (physics) 

[Submitted on 16 Jul 2023]

# Title:Role of Pore Dilation in Molecular Transport through the Nuclear Pore Complex: Insights from Polymer Scaling Theory

Authors:[Atsushi Matsuda](https://arxiv.org/search/physics?searchtype=author&query=Matsuda,+A), [Mohammad R. K. Mofrad](https://arxiv.org/search/physics?searchtype=author&query=Mofrad,+M+R+K)

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> Abstract:Recent studies have suggested that the Nuclear Pore Complex (NPC) plays a significant role in mechanotransduction. When a force is exerted, the NPC's diameter widens, leading to an increased molecular flux into the nucleus. In this study, we sought to further explore this phenomenon and quantitativelly assess the impact of pore dilation on molecular transport through the NPC. Utilizing the scaling theory of polymers, we developed a theoretical model to examine the relationship between pore size and the molecular transport rate. Our model posits that the mesh structure inside the pore, formed by FG-Nups, significantly influences the transport rate. Consequently, we propose that the transport rate is exponentially related to the pore size. To validate our model, we conducted extensive Brownian dynamics simulations. Our results demonstrated that the model accurately represents the transport dynamics except for exceptionally small molecules. For these molecules, the local mesh structure becomes less significant, and instead, they perceive the global structure of the pore. We also identified a critical threshold value, which allows for an estimation of whether a given molecule falls within the scope of our model. Our findings provide valuable insights into the dynamics of molecular transport in the NPC and pave the way for future research on the NPC's role in mechanotransduction. 

Subjects: |  Biological Physics (physics.bio-ph)  
---|---  
Cite as: | [arXiv:2307.07959](https://arxiv.org/abs/2307.07959) [physics.bio-ph]  
  | (or  [arXiv:2307.07959v1](https://arxiv.org/abs/2307.07959v1) [physics.bio-ph] for this version)   
  |  <https://doi.org/10.48550/arXiv.2307.07959> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Atsushi Matsuda [[view email](/show-email/561809f0/2307.07959)]   
**[v1]** Sun, 16 Jul 2023 06:40:48 UTC (4,323 KB)  


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url: http://arxiv.org/abs/2412.11189v2
title: [2412.11189v2] Leveraging Large Language Models for Active Merchant Non-player Characters
scrape_date: 2025-03-11T15:37:03.509387
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# Computer Science > Artificial Intelligence

**arXiv:2412.11189v2** (cs) 

[Submitted on 15 Dec 2024 ([v1](https://arxiv.org/abs/2412.11189v1)), last revised 8 Jan 2025 (this version, v2)]

# Title:Leveraging Large Language Models for Active Merchant Non-player Characters

Authors:[Byungjun Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+B), [Minju Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+M), [Dayeon Seo](https://arxiv.org/search/cs?searchtype=author&query=Seo,+D), [Bugeun Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+B)

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> Abstract:We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions have been a focus, negotiations between merchant NPCs and players on item prices have not received sufficient attention. First, we define passive pricing as the limited ability of merchants to modify predefined item prices. Second, passive communication means that merchants can only interact with players in a scripted manner. To tackle these issues and create an active merchant NPC, we propose a merchant framework based on large language models (LLMs), called MART, which consists of an appraiser module and a negotiator module. We conducted two experiments to guide game developers in selecting appropriate implementations by comparing different training methods and LLM sizes. Our findings indicate that finetuning methods, such as supervised finetuning (SFT) and knowledge distillation (KD), are effective in using smaller LLMs to implement active merchant NPCs. Additionally, we found three irregular cases arising from the responses of LLMs. We expect our findings to guide developers in using LLMs for developing active merchant NPCs. 

Comments: | Under review / Modified the links to code and dataset  
---|---  
Subjects: |  Artificial Intelligence (cs.AI); Computation and Language (cs.CL)  
Cite as: | [arXiv:2412.11189](https://arxiv.org/abs/2412.11189) [cs.AI]  
  | (or  [arXiv:2412.11189v2](https://arxiv.org/abs/2412.11189v2) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2412.11189> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Bugeun Kim [[view email](/show-email/d28047b1/2412.11189)]   
**[[v1]](/abs/2412.11189v1)** Sun, 15 Dec 2024 13:48:39 UTC (780 KB)  
**[v2]** Wed, 8 Jan 2025 11:24:17 UTC (818 KB)  


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url: https://arxiv.org/abs/2412.05631
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hash: c8def2fc68e9f4d913139e52a8535b7781b5974bb341e91b64106eb0b6eae2d0
title:
[2412.05631] CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds

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# Computer Science > Computation and Language

**arXiv:2412.05631** (cs) 

[Submitted on 7 Dec 2024]

# Title:CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds

Authors:[Lei Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+L), [Jianxun Lian](https://arxiv.org/search/cs?searchtype=author&query=Lian,+J), [Yi Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+Y), [Yanqi Dai](https://arxiv.org/search/cs?searchtype=author&query=Dai,+Y), [Haoxuan Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+H), [Xu Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+X), [Xing Xie](https://arxiv.org/search/cs?searchtype=author&query=Xie,+X), [Ji-Rong Wen](https://arxiv.org/search/cs?searchtype=author&query=Wen,+J)

View a PDF of the paper titled CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds, by Lei Wang and 7 other authors

[View PDF](/pdf/2412.05631)

> Abstract:Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, and emotional companions. Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as maintaining character fidelity throughout a storyline and navigating open-ended narratives without a definitive ground truth. Current evaluation methods, which primarily focus on question-answering or conversational snapshots, fall short of adequately capturing the nuanced character traits and behaviors essential for authentic role-playing. In this paper, we propose CharacterBox, which is a simulation sandbox designed to generate situational fine-grained character behavior trajectories. These behavior trajectories enable a more comprehensive and in-depth evaluation of role-playing capabilities. CharacterBox consists of two main components: the character agent and the narrator agent. The character agent, grounded in psychological and behavioral science, exhibits human-like behaviors, while the narrator agent coordinates interactions between character agents and environmental changes. Additionally, we introduce two trajectory-based methods that leverage CharacterBox to enhance LLM performance. To reduce costs and facilitate the adoption of CharacterBox by public communities, we fine-tune two smaller models, CharacterNR and CharacterRM, as substitutes for GPT API calls, and demonstrate their competitive performance compared to advanced GPT APIs. 

Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
---|---  
Cite as: | [arXiv:2412.05631](https://arxiv.org/abs/2412.05631) [cs.CL]  
  | (or  [arXiv:2412.05631v1](https://arxiv.org/abs/2412.05631v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2412.05631> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Lei Wang [[view email](/show-email/b7fd3d6b/2412.05631)]   
**[v1]** Sat, 7 Dec 2024 12:09:35 UTC (1,388 KB)  


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url: http://arxiv.org/abs/2407.03460v1
title: [2407.03460v1] Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
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# Computer Science > Computation and Language

**arXiv:2407.03460v1** (cs) 

[Submitted on 3 Jul 2024]

# Title:Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

Authors:[Sudha Rao](https://arxiv.org/search/cs?searchtype=author&query=Rao,+S), [Weijia Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+W), [Michael Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+M), [Jorge Leandro](https://arxiv.org/search/cs?searchtype=author&query=Leandro,+J), [Ken Lobb](https://arxiv.org/search/cs?searchtype=author&query=Lobb,+K), [Gabriel DesGarennes](https://arxiv.org/search/cs?searchtype=author&query=DesGarennes,+G), [Chris Brockett](https://arxiv.org/search/cs?searchtype=author&query=Brockett,+C), [Bill Dolan](https://arxiv.org/search/cs?searchtype=author&query=Dolan,+B)

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> Abstract:The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed. In this paper, we seek to understand how human players collaborate with LLM-driven NPCs to accomplish in-game goals. We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. We perform a user study in which 28 Minecraft players play this minigame and share their feedback. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. We also report on the current limitations of language-only models that do not have rich game-state or visual understanding. We believe that this preliminary study and analysis will inform future game developers on how to better exploit these rapidly improving generative AI models for collaborative roles in games. 

Comments: | Accepted at Wordplay workshop at ACL 2024  
---|---  
Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:2407.03460](https://arxiv.org/abs/2407.03460) [cs.CL]  
  | (or  [arXiv:2407.03460v1](https://arxiv.org/abs/2407.03460v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2407.03460> Focus to learn more arXiv-issued DOI via DataCite  
Journal reference: | ACL 2024  
  
## Submission history

From: Sudha Rao [[view email](/show-email/6a236d2c/2407.03460)]   
**[v1]** Wed, 3 Jul 2024 19:11:21 UTC (8,517 KB)  


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url: http://arxiv.org/abs/2307.15833v1
title: [2307.15833v1] Dialogue Shaping: Empowering Agents through NPC Interaction
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# Computer Science > Computation and Language

**arXiv:2307.15833v1** (cs) 

[Submitted on 28 Jul 2023]

# Title:Dialogue Shaping: Empowering Agents through NPC Interaction

Authors:[Wei Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+W), [Xiangyu Peng](https://arxiv.org/search/cs?searchtype=author&query=Peng,+X), [Mark Riedl](https://arxiv.org/search/cs?searchtype=author&query=Riedl,+M)

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> Abstract:One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially in text-based game environments where the action space is extensive. However, non-player characters (NPCs) sometimes hold some key information about the game, which can potentially help to train RL agents faster. Thus, this paper explores how to interact and converse with NPC agents to get the key information using large language models (LLMs), as well as incorporate this information to speed up RL agent's training using knowledge graphs (KGs) and Story Shaping. 

Subjects: |  Computation and Language (cs.CL)  
---|---  
Cite as: | [arXiv:2307.15833](https://arxiv.org/abs/2307.15833) [cs.CL]  
  | (or  [arXiv:2307.15833v1](https://arxiv.org/abs/2307.15833v1) [cs.CL] for this version)   
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## Submission history

From: Wei Zhou [[view email](/show-email/6d552a1a/2307.15833)]   
**[v1]** Fri, 28 Jul 2023 22:44:54 UTC (2,500 KB)  


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url: https://arxiv.org/abs/2407.03460
title: [2407.03460] Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
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# Computer Science > Computation and Language

**arXiv:2407.03460** (cs) 

[Submitted on 3 Jul 2024]

# Title:Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

Authors:[Sudha Rao](https://arxiv.org/search/cs?searchtype=author&query=Rao,+S), [Weijia Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+W), [Michael Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+M), [Jorge Leandro](https://arxiv.org/search/cs?searchtype=author&query=Leandro,+J), [Ken Lobb](https://arxiv.org/search/cs?searchtype=author&query=Lobb,+K), [Gabriel DesGarennes](https://arxiv.org/search/cs?searchtype=author&query=DesGarennes,+G), [Chris Brockett](https://arxiv.org/search/cs?searchtype=author&query=Brockett,+C), [Bill Dolan](https://arxiv.org/search/cs?searchtype=author&query=Dolan,+B)

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> Abstract:The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed. In this paper, we seek to understand how human players collaborate with LLM-driven NPCs to accomplish in-game goals. We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. We perform a user study in which 28 Minecraft players play this minigame and share their feedback. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. We also report on the current limitations of language-only models that do not have rich game-state or visual understanding. We believe that this preliminary study and analysis will inform future game developers on how to better exploit these rapidly improving generative AI models for collaborative roles in games. 

Comments: | Accepted at Wordplay workshop at ACL 2024  
---|---  
Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:2407.03460](https://arxiv.org/abs/2407.03460) [cs.CL]  
  | (or  [arXiv:2407.03460v1](https://arxiv.org/abs/2407.03460v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2407.03460> Focus to learn more arXiv-issued DOI via DataCite  
Journal reference: | ACL 2024  
  
## Submission history

From: Sudha Rao [[view email](/show-email/6a236d2c/2407.03460)]   
**[v1]** Wed, 3 Jul 2024 19:11:21 UTC (8,517 KB)  


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url: https://arxiv.org/abs/2502.00595
title: [2502.00595] RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines
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# Computer Science > Computation and Language

**arXiv:2502.00595** (cs) 

[Submitted on 1 Feb 2025]

# Title:RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines

Authors:[Pengfei Yu](https://arxiv.org/search/cs?searchtype=author&query=Yu,+P), [Dongming Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+D), [Silin Meng](https://arxiv.org/search/cs?searchtype=author&query=Meng,+S), [Jaewon Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+J), [Weisu Yin](https://arxiv.org/search/cs?searchtype=author&query=Yin,+W), [Andrea Yaoyun Cui](https://arxiv.org/search/cs?searchtype=author&query=Cui,+A+Y), [Zhenlin Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+Z), [Yi Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+Y), [Xingjian Shi](https://arxiv.org/search/cs?searchtype=author&query=Shi,+X), [Mu Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+M), [Alex Smola](https://arxiv.org/search/cs?searchtype=author&query=Smola,+A)

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> Abstract:We present RPGBench, the first benchmark designed to evaluate large language models (LLMs) as text-based role-playing game (RPG) engines. RPGBench comprises two core tasks: Game Creation (GC) and Game Simulation (GS). In GC, an LLM must craft a valid and playable RPG world using a structured event-state representation, ensuring logical coherence and proper termination conditions. In GS, the LLM simulates interactive gameplay across multiple rounds while consistently updating states and enforcing game rules. To comprehensively assess performance, RPGBench integrates objective and subjective evaluation methodologies. Objective measures verify adherence to event mechanics and check variable updates without requiring human intervention. Subjective measures, such as content interestingness, action quality, and role-playing capability, are evaluated via an LLM-as-a-judge framework, where a strong LLM grades each candidate's outputs. Empirical results demonstrate that state-of-the-art LLMs can produce engaging stories but often struggle to implement consistent, verifiable game mechanics, particularly in long or complex scenarios. By combining structured, rule-based assessments with LLM-based judgments, RPGBench provides a new standard for evaluating how well LLMs can balance creativity, coherence, and complexity in text-based RPGs, opening avenues for more immersive and controllable interactive storytelling. 

Comments: | Submitted to ICML 2025  
---|---  
Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
Cite as: | [arXiv:2502.00595](https://arxiv.org/abs/2502.00595) [cs.CL]  
  | (or  [arXiv:2502.00595v1](https://arxiv.org/abs/2502.00595v1) [cs.CL] for this version)   
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## Submission history

From: Pengfei Yu [[view email](/show-email/fbd5dac1/2502.00595)]   
**[v1]** Sat, 1 Feb 2025 23:40:24 UTC (2,992 KB)  


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url: http://arxiv.org/abs/2310.14867v1
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[2310.14867v1] Who's Watching Me?: Exploring the Impact of Audience Familiarity on Player Performance, Experience, and Exertion in Virtual Reality Exergames

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# Computer Science > Human-Computer Interaction

**arXiv:2310.14867v1** (cs) 

[Submitted on 23 Oct 2023]

# Title:Who's Watching Me?: Exploring the Impact of Audience Familiarity on Player Performance, Experience, and Exertion in Virtual Reality Exergames

Authors:[Zixuan Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+Z), [Wenge Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+W), [Jialin Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J), [Hongyu Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+H), [Cheng-Hung Lo](https://arxiv.org/search/cs?searchtype=author&query=Lo,+C), [Hai-Ning Liang](https://arxiv.org/search/cs?searchtype=author&query=Liang,+H)

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> Abstract:Familiarity with audiences plays a significant role in shaping individual performance and experience across various activities in everyday life. This study delves into the impact of familiarity with non-playable character (NPC) audiences on player performance and experience in virtual reality (VR) exergames. By manipulating of NPC appearance (face and body shape) and voice familiarity, we explored their effect on game performance, experience, and exertion. The findings reveal that familiar NPC audiences have a positive impact on performance, creating a more enjoyable gaming experience, and leading players to perceive less exertion. Moreover, individuals with higher levels of self-consciousness exhibit heightened sensitivity to the familiarity with NPC audiences. Our results shed light on the role of familiar NPC audiences in enhancing player experiences and provide insights for designing more engaging and personalized VR exergame environments. 

Comments: | 10 pages, 5 figures, IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2023  
---|---  
Subjects: |  Human-Computer Interaction (cs.HC)  
Cite as: | [arXiv:2310.14867](https://arxiv.org/abs/2310.14867) [cs.HC]  
  | (or  [arXiv:2310.14867v1](https://arxiv.org/abs/2310.14867v1) [cs.HC] for this version)   
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## Submission history

From: Zixuan Guo [[view email](/show-email/d2f46261/2310.14867)]   
**[v1]** Mon, 23 Oct 2023 12:37:02 UTC (18,973 KB)  


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url: https://news.ycombinator.com/item?id=40315434
title: Player-Driven Emergence in LLM-Driven Game Narrative | Hacker News
scrape_date: 2025-03-11T15:37:03.772111
content_type: markdown
hash: e100506e8d6bd310fcd51ac735f991094b42c851ff830ce41ec864e988600182
content:
| [![](y18.svg)](https://news.ycombinator.com) | **[Hacker News](news)** [new](newest) | [past](front) | [comments](newcomments) | [ask](ask) | [show](show) | [jobs](jobs) | [submit](submit) |  [login](login?goto=item%3Fid%3D40315434)  
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|  | [](vote?id=40315434&how=up&goto=item%3Fid%3D40315434)| [Player-Driven Emergence in LLM-Driven Game Narrative](https://arxiv.org/abs/2404.17027) ([arxiv.org](from?site=arxiv.org))  
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|  118 points by [benbreen](user?id=benbreen) [10 months ago](item?id=40315434) | [hide](hide?id=40315434&goto=item%3Fid%3D40315434) | [past](https://hn.algolia.com/?query=Player-Driven%20Emergence%20in%20LLM-Driven%20Game%20Narrative&type=story&dateRange=all&sort=byDate&storyText=false&prefix&page=0) | [favorite](fave?id=40315434&auth=a112f2909818660c7bbda57a54e84feaee820677) | [122 comments](item?id=40315434)  
  
  
| ![](s.gif)|  [](vote?id=40315623&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40315623) | next [[–]](javascript:void\(0\))   
Obviously there's a focus on how LLMs will play into the main narrative... but what I'm more excited for, honestly, is how we can integrate them with NPCs and background narratives. An LLM could generate traits, backstories, narratives, etc for any number of NPCs; rewrite those narratives based on player actions when they encounter that NPC, or other related NPCs; use those updated narratives to create new NPCs, etc.Very exciting times ahead in game design!  
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| ![](s.gif)|  [](vote?id=40315649&how=up&goto=item%3Fid%3D40315434) |  [kromem](user?id=kromem) [10 months ago](item?id=40315649) | parent | next [[–]](javascript:void\(0\))   
I can't wait for a subscription based open world where the paths of other players caches the generations for the next ones.So your subscription pays for generations, but also fills out the persistent narrative and lore.I actually like the 3 choices dialogue system and don't want to come up with my own NPC small talk on my couch, but the idea that any NPC that's been talked to by any player gradually has a whole catalogue of interactions to pull from means there would be compounding depth to the world as more players interact.Like _Minecraft_ but where it's collective world building by exploration and interaction rather than actual building it yourself.Humans tend to be pretty predictable, so I feel like the universe would quickly feel like it's insanely deep after just a short while with a somewhat decent player base.  
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| ![](s.gif)|  [](vote?id=40315864&how=up&goto=item%3Fid%3D40315434) |  [guitarlimeo](user?id=guitarlimeo) [10 months ago](item?id=40315864) | root | parent | next [[–]](javascript:void\(0\))   
Even if the dialogue wouldn't be generic like ChatGPT tends to be, I would find this incredibly boring. With so many options all the options become meaningless to me. But to each their own I guess - I've always liked games with linear storylines more than the ones where you get to choose your own path.Also I have a feeling that the NPC narratives when enriched by all player interactions would tend to go towards some average, i.e. become more and more generic and dull. The truly rich experiences have always been on the fringes in all art for me. I'm interested to see a counter example to this though.  
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| ![](s.gif)|  [](vote?id=40316042&how=up&goto=item%3Fid%3D40315434) |  [selalipop](user?id=selalipop) [10 months ago](item?id=40316042) | root | parent | next [[–]](javascript:void\(0\))   
I'm working on a website [1] that's essentially "Choose your own adventure with AI NPCs" and I've found two things:a) LLMs are excellent at keeping a "linear enough" storyline without being linear. They'll let you do outlandish things, but given the assignment of "tell a cohesive story" they manage to corral the story back to something sensible unless the player intentionally keeps pushing at the boundary (in which case they probably do want things to go off the rails)b) LLMs can do delightfully colorful dialogue, they just need to be grounded in a character. Everyone thinks of factual grounding, but given enough description of speech patterns, character motivations, etc. they're capable of dialogue that's lively and completely rid of "GPT-isms", which are what tend to break immersionI actually trained an open model [2] on the task of grounding LLMs in characters and actions as opposed to factual things like RAG, and eventually I want to build a game demo out of it[1] [https://www.tryspellbound.com](https://www.tryspellbound.com) [2] [https://huggingface.co/hf-100/mistral-spellbound-research](https://huggingface.co/hf-100/mistral-spellbound-research)  
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| ![](s.gif)|  [](vote?id=40318051&how=up&goto=item%3Fid%3D40315434) |  [CuriouslyC](user?id=CuriouslyC) [10 months ago](item?id=40318051) | root | parent | next [[–]](javascript:void\(0\))   
If you switch models you don't have to work so hard to get rid of GPTisms. Llama takes characters with way less work.  
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| ![](s.gif)|  [](vote?id=40320951&how=up&goto=item%3Fid%3D40315434) |  [selalipop](user?id=selalipop) [10 months ago](item?id=40320951) | root | parent | next [[–]](javascript:void\(0\))   
I've experimented with 30 or models so far, my general finding is closed source models like Claude have GPT-isms, while open source models do have a little less of a default tone but their ability to understand existing worlds is directly tied to how many tokens they were trained on.Since existing worlds are (currently) where most of the stories are set, it's worth it to use a closed source models and wrangle their issues with dialogue.To it's credit though, Llama 3 is the first OSS model trained on enough tokens to not feel lost for most worlds, so I've started routing some traffic to it for free usersThe output format the site uses is also really really hard for most models to follow without fine-tuning, but fine-tuning then causes them to pick up the vocabulary of whichever model they were fine tuned on, which is a bit unfortunate  
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| ![](s.gif)|  [](vote?id=40316736&how=up&goto=item%3Fid%3D40315434) |  [guitarlimeo](user?id=guitarlimeo) [10 months ago](item?id=40316736) | root | parent | prev | next [[–]](javascript:void\(0\))   
Nice project! I tried it out and it was fun, you definitely got rid of the GPT-isms so good work.Didn't go through the registering phase cause I couldn't find any info on what info you store and on the pricing. Could you provide that?  
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| ![](s.gif)|  [](vote?id=40316931&how=up&goto=item%3Fid%3D40315434) |  [selalipop](user?id=selalipop) [10 months ago](item?id=40316931) | root | parent | next [[–]](javascript:void\(0\))   
Privacy policy and terms of service should be visible on homepage and in the sign up dialogThe pricing page is also visible when signed in and in a story (no dedicated pricing page yet but it's in the backlog)  
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| ![](s.gif)|  [](vote?id=40317008&how=up&goto=item%3Fid%3D40315434) |  [stoorafa](user?id=stoorafa) [10 months ago](item?id=40317008) | root | parent | prev | next [[–]](javascript:void\(0\))   
Really cool project. When I got to the sign in page, the email address I would have given my (edit: Google account) info seemed fishy, like it was a random string of letters. Any way to make it seem more…inviting?  
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| ![](s.gif)|  [](vote?id=40320917&how=up&goto=item%3Fid%3D40315434) |  [selalipop](user?id=selalipop) [10 months ago](item?id=40320917) | root | parent | next [[–]](javascript:void\(0\))   
Unfortunately Supabase charges extra for the luxury of setting that URL, and the site is wildly unprofitable right now so I'm sticking to their free offering for the time being  
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| ![](s.gif)|  [](vote?id=40316451&how=up&goto=item%3Fid%3D40315434) |  [Nevermark](user?id=Nevermark) [10 months ago](item?id=40316451) | root | parent | prev | next [[–]](javascript:void\(0\))   
> become more and more generic and dullObviously we don't want experience averaging NPCs.> Mad Hatter’s first goal is to ask the player to humor him with a joke.This is ok for simple stylized, small world, single storyline games, but not for open worlds.For that, NPCs need their own motivations, so they essentially play the game too. With needs like maintain their smithy, so they can build quality armor, so they can make customers happy, so they can make money, so they can feed their family. I.e. not just being props.And flexible in how they adjust subgoals to meet their core goals, relative to player interaction: such as being convinced to go on a trek as an armorer, or on a search to find and extract rare materials for a magical shield. Willing to fight in revenge for their home town's sacking.Westworld got NPC dynamics right.  
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| ![](s.gif)|  [](vote?id=40316699&how=up&goto=item%3Fid%3D40315434) |  [guitarlimeo](user?id=guitarlimeo) [10 months ago](item?id=40316699) | root | parent | next [[–]](javascript:void\(0\))   
Ah Westworld is a good example! I liked the series and the premise, but I still wouldn't liken the park to a good game - it's a theme park with nice attractions.As c048 said best ([https://news.ycombinator.com/item?id=40316308](https://news.ycombinator.com/item?id=40316308)), good art needs direction and focus, and AI NPCs being able to do anything they want in an open world doesn't make for a good story. It could make for a good sandbox with emergent gameplay, sure. But my point was that there are people who yearn for a good story, and a good story doesn't have many options in it, otherwise the story's beats would become meaningless.Hmm. Maybe with this the problem Rockstar has with their linear missions mixed with open world could be fixed a bit - i.e. the open world NPCs could react in story missions to what you've done in free roam. This would lessen the cognitive dissonance the NPCs seem to have when you can now blow a town into pieces and then start the story mission and no one cares.  
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| ![](s.gif)|  [](vote?id=40323596&how=up&goto=item%3Fid%3D40315434) |  [Nevermark](user?id=Nevermark) [10 months ago](item?id=40323596) | root | parent | next [[–]](javascript:void\(0\))   
I like the idea of challenging uber goals, that progress in a series, but each with a deep tree of alternate subgoals/solutions.Subgoals that are resource, constraint defined, could make very flexible solutions. For instance, if you need help with some subgoal, it is going to play out very different depending on what NPCs you have established credibility with before, and their skills and dynamics.If you need money, then how you get that money is also going to depend on your history, knowledge of a city, previous connections, etc.That could provide an overall story arc, but with a very open world and fully functional NPC experience.So: organized series and trees of goals. Open ended solutions.  
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| ![](s.gif)|  [](vote?id=40316792&how=up&goto=item%3Fid%3D40315434) |  [littlestymaar](user?id=littlestymaar) [10 months ago](item?id=40316792) | root | parent | prev | next [[–]](javascript:void\(0\))   
> For that, NPCs need their own motivations, so they essentially play the game too. With needs like maintain their smithy, so they can build quality armor, so they can make customers happy, so they can make money, so they can feed their family. I.e. not just being propsSo you want a system like rimworld, and a LLM doesn't really help on that front. It can power the dialogues, but that's it, all the logic you describe would need to be encoded somewhere else.  
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| ![](s.gif)|  [](vote?id=40319400&how=up&goto=item%3Fid%3D40315434) |  [eddd-ddde](user?id=eddd-ddde) [10 months ago](item?id=40319400) | root | parent | prev | next [[–]](javascript:void\(0\))   
To each their own indeed. I was never a fan of linear games.In Skyrim it took me multiple years to finally complete the main story line. I would always look for books, store potion ingredients, walk in the forest...There's a big role play component in this. The game could learn from the players RP and improve.  
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| ![](s.gif)|  [](vote?id=40320114&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40320114) | parent | prev | next [[–]](javascript:void\(0\))   
No! This is a terrible, terrible idea. At best you can have the LLM generate minor variations of the nothings they say.1) you need to be able to ensure that the characters don’t hallucinate new details. No big deal.2) you need to be able to ensure that the characters don’t suggest they can or will do anything that they can’t do. For NPCs, this is typically literally nothing. They can’t do anything. It’s actually way worse for realism and immersion if every character has a unique reason why they can’t do anything than every single guard citing their arrow to the knee and leaving it at that.3) NPCs for story reasons often should know something but don’t tell you it. It’s very difficult to execute any sort of story flow if your characters are suddenly free agents. At best they merely lack self consistency. Stories are not going to become better if you can meta game them as genre savvy speculators.4) NPCs with irrelevant backstories are very dull. They are absolutely annoying to talk to. We have these in DND. And you may say “hey, I loved my DND NPC”, but that character probably started to become very important as consistent because of the DM’s attention. Which is cool for DND, but it’s not cool for a structured game.Like, would Harry Potter be a better experience if you could personally grill dumbledore for information in the middle of the first book? I think 100% no.Dumb NPCs are an aspect of the suspension of disbelief of game writing. And it largely works. Asking for LLM agents is like asking to remove the need for the suspension of disbelief. But most narratives are too fragile for this to happen.Narratives generally follow a flow that ultimately concludes after a climax. If you give players more agency the most likely outcome is that they follow a path that does not hit the climax.  
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| ![](s.gif)|  [](vote?id=40320977&how=up&goto=item%3Fid%3D40315434) |  [Hugsun](user?id=Hugsun) [10 months ago](item?id=40320977) | root | parent | next [[–]](javascript:void\(0\))   
This seems very pessimistic to me. For a linear storyline, highly dynamic NPCs are likely not going to add to the experience, that is correct. There are however other ways of creating interesting narratives.A good example is dwarf fortress where the narratives are created very dynamically and the player is free to interact with them as they want. LLMs could add immensely to these types of games.  
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| ![](s.gif)|  [](vote?id=40321237&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40321237) | root | parent | next [[–]](javascript:void\(0\))   
Still no. Your dwarves fundamentally have nothing interesting to say. It might be minority amusing that a bot can say “hey I just caused a lava flow and I feel bad about it”. But it’s not better.Dwarf fortress creates great stories because YOU are filling in the blanks. It would be MUCH WORSE if you could no longer fill in the blanks because some ai keeps breaking your head canon.  
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| ![](s.gif)|  [](vote?id=40323836&how=up&goto=item%3Fid%3D40315434) |  [Hugsun](user?id=Hugsun) [10 months ago](item?id=40323836) | root | parent | next [[–]](javascript:void\(0\))   
In my eyes, Dwarf fortresses interesting stories emerge from the events that happen within the game. It doesn't require any blank filling, although it doesn't suffer if you do it either.> ai keeps breaking your head canon.We must be fundamentally different types of people. I can't imagine even having this thought. If I'm playing a game that has characters, I'm not going to make up some facts about them and be upset when I talk to them and those made up facts turn out to be wrong. This is the only interpretation of your words that I can think of. Feel free to confirm it or deny.  
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| ![](s.gif)|  [](vote?id=40323930&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40323930) | root | parent | next [[–]](javascript:void\(0\))   
You’ve never intuited that two characters are friends? Or perhaps that guy holding a ton of cheese is the cheese guy? Or that an event was emotionally meaningful to them?I don’t know what you’re hoping to talk to these dwarves about.I’m not saying I would be “upset” if the head canon wasn’t actualized. I’m saying I would be underwhelmed because the generic LLM content is nearly guaranteed by this other thing that I stated as a premise I found interesting or amusing.  
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| ![](s.gif)|  [](vote?id=40325454&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325454) | root | parent | next [[–]](javascript:void\(0\))   
If you intuit that two characters are friends, and then something happens that makes it seem like they're not friends, you'll say "Hey, I thought you two were friends", and the LLM will realize what you're going for, and then come up with something like "Oh we were but now we're fighting".The LLM's role will very much a "yes, and" improv role that will let you guide your own personal story. Or have it push back on you if you want. "What? You thought we were friends? No, I was just doing my duties".What I want to talk about with the dwarves is everything, and LLMs will deliver in spades. I want to ask the lowly NPC what life is like working hard in the mines. Then I want to decide to recruit him on my quest because I like his backstory and personality. Then I'll watch as he levels up with me, in a way that could never be scripted by the game creators. That's just one small way that LLMs will provide a far superior gameplay experience.  
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| ![](s.gif)|  [](vote?id=40327340&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40327340) | root | parent | next [[–]](javascript:void\(0\))   
> If you intuit that two characters are friends, and then something happens that makes it seem like they're not friends, you'll say "Hey, I thought you two were friends", and the LLM will realize what you're going for, and then come up with something like "Oh we were but now we're fighting".That’s the opposite of what I’m saying. Asking the LLM to justify something is possible but, imo, very boring.What I’m complaining about is asking about their friend and they don’t believe they have a friend. They just fail to capture the apparent narrative that you’ve enjoyed because it’s not really close to whatever game logic they’re scripted to utilize  
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| ![](s.gif)|  [](vote?id=40327847&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40327847) | root | parent | next [[–]](javascript:void\(0\))   
There's no game logic they're scripted to utilize that limits what you're looking for.I'm not sure why you think they'd say they have no friends. You'd say "Tell me about your friend", and the NPC would respond with something like "I have many friends, which one do you mean?". Then you could say "The one I saw you walking into town with yesterday", and the NPC might say, "Oh Rolf, him and I go way back!".Maybe he says that because the LLM has instructions that him and Rolf are friends explicitly, put there by the game devs. Maybe it's not there explicitly, but the LLM sees what you mean and writes the backstory dynamically. Maybe a little bit of both.LLMs are great at picking up narratives like that. It's kind of their whole thing.  
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| ![](s.gif)|  [](vote?id=40329449&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40329449) | root | parent | next [[–]](javascript:void\(0\))   
Does that really sound fun to you? Why would a player want to grill an NPC about their friend Rolf if all that they're going to get from it is improvised LLM-generated anecdotes?Players don't talk to NPCs simply because they want to see text on the screen. They're hoping for well-written and interesting dialogue. If the devs couldn't be bothered to write lines for this guy, why would I want to talk to him? He clearly has nothing to say worth listening to.Have you actually tried asking ChatGPT to tell you a story? It's boring. It's not a good writer. I don't know why you think players would want to spend time reading that stuff.  
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| ![](s.gif)|  [](vote?id=40331412&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40331412) | root | parent | next [[–]](javascript:void\(0\))   
> devs couldn't be botheredI think you just don't get it, and maybe this genre wouldn't be for you, which is fine. It's not about the devs bothering to write lines, it's about the devs enabling you to create your own world. I don't want some shitty NPCs slapped in there by a dev trying to hit a deadline. I want to experience my own story, freed from the shackles of the on-rails experience that game devs must currently provide.> Have you actually tried asking ChatGPT to tell you a story?What you're not getting is that the storytelling will be cooperative. You'll still be able to consume content to your heart's content. Some of that content will soon be people creating compelling stories in cooperation with LLMs. LLMs will also be great at storytelling too, but the best stories are ones that you can help write yourself.  
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| ![](s.gif)|  [](vote?id=40338577&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40338577) | root | parent | next [[–]](javascript:void\(0\))   
> I want to experience my own storyThen why not write your own story? An LLM can't write your story for you. They're very bad writers. It can't even help much—its contributions are going to be dull and uninspired.> the best stories are ones that you can help write yourselfIf you're trying to co-create a story with someone in a play environment, the best analogy would be D&D or another role-playing game. And playing D&D with ChatGPT would be boring and unfun. It's just not a good storyteller. It's going to give you shit material. You'd be better-off cutting it out of the equation and just writing a story alone.> LLMs will also be great at storytelling tooWill they? They're godawful at it right now, and they're not showing much sign of improving. Let's discuss the tech as it currently exists, rather than some speculative future version of it with imaginary capabilities.> What you're not getting is that the storytelling will be cooperative.No, I get that. But why would I want to cooperatively tell a story with a bad storyteller? I'd rather tell a story alone.  
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| ![](s.gif)|  [](vote?id=40328589&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40328589) | root | parent | prev | next [[–]](javascript:void\(0\))   
A bot that always affirms your assertions is boring as hell to talk to.If the dwarf bot does not know that the character he represents spent the last 24 hours in combat alongside another specific dwarf without you communicating this in text then it might as well not be attached to any character. Liked what would the point be??? That’s just an out of game bot.Your example conversation of leading the bot to say specific things is… awful in my opinion. Who would want to talk to an NPC like this?  
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| ![](s.gif)|  [](vote?id=40331436&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40331436) | root | parent | next [[–]](javascript:void\(0\))   
So don't use an LLM that always affirms your assertions. Easy peasy.I don't understand your beef. My awkward dialogue was just to demonstrate that the LLM can pick up on all of these things you claim it can't. The LLM wouldn't "believe it didn't have a friend", why would you think that?It's also fine to just enjoy on-rails games with a prewritten story, those will still exist. You might even enjoy the epic stories that people create in these new sandboxes and share with the world.  
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| ![](s.gif)|  [](vote?id=40332147&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40332147) | root | parent | next [[–]](javascript:void\(0\))   
Because your mental model of how this would work is handwaving the impossibly difficult task of converging the LLM state with the narrative and game state.Your crappy awkward dialogue is probably a very realistic example of a stupid bot that doesn’t even know what actor it represents in game.You’re asking why if would think that? You tell me how it would come to the conclusion correctly by using the game logic. And don’t give an answer that requires a hard coded dev interface between specific game elements and the LLM because that is on its own a more complex task than the LLM itself. It’s incredibly difficult. Shitty home assistants that can’t actually do anything being the prime example. The language processing is not the hard part. It’s the mapping of language to data and capabilities that’s extremely hard.Synthesizing an agent that can form an identity based on its experiences and leverage game mechanics to pursue goals and convey all of this logic in flexible natural language is basically asking for a general intelligence. It’s an incredibly different thing from the shitty dialogue example you wrote  
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| ![](s.gif)|  [](vote?id=40332428&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40332428) | root | parent | next [[–]](javascript:void\(0\))   
It's asking for an LLM, which we have. I really don't think you understand how LLMs work or something, I'm not really sure.I also think that you're intentionally missing the point so that you can be insulting.May you find games to play you enjoy.  
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| ![](s.gif)|  [](vote?id=40326133&how=up&goto=item%3Fid%3D40315434) |  [dragonwriter](user?id=dragonwriter) [10 months ago](item?id=40326133) | root | parent | prev | next [[–]](javascript:void\(0\))   
You seem to view the only way computer games can be enjoyable is aa tightly-scripted near-movies. And that's certainly one model, but hardly (even without LLMs) the only model that has proven enjoyable. LLMs potentially (I think they’ve got a wat to go before this is really viable, but the potential is there) open up new _styles_ of conputer gaming.  
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| ![](s.gif)|  [](vote?id=40328609&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40328609) | root | parent | next [[–]](javascript:void\(0\))   
It’s not really new. It’s just a social role play without the social aspect. Which is worse.Games don’t need to be near movies but they should have an either a cohesive plot or no plot. A shitty made up on the fly plot is just not going to work  
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| ![](s.gif)|  [](vote?id=40322869&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40322869) | root | parent | prev | next [[–]](javascript:void\(0\))   
> Your dwarves fundamentally have nothing interesting to say.This can be said about all NPCs, yet we bother to script them, and I would hope nobody would suggest we just remove NPC dialogue.  
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| ![](s.gif)|  [](vote?id=40325130&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40325130) | root | parent | next [[–]](javascript:void\(0\))   
There's an idea in game design called "negative possibility space." If your player walks to the end of a long hallway and it's a dead end, or they shimmy up a narrow ledge to the top of a building only to find nothing, they'll be disappointed. So either stick something interesting there, or block it off. This applies to NPC dialogue too. If an NPC can be spoken to, they should have something interesting to say. If they don't, you shouldn't be able to talk to them.Giving a chatbot brain to an NPC who would otherwise only get a single generic line to say creates an infinite amount of negative possibility space. A player can waste a literally infinite amount of time probing a chatbot NPC for narratively interesting details, and all they'll get is chatbot slop. Now it doesn't matter if you put something interesting at the end of the hallway: You've wasted so much of the player's time now that they no longer trust you to. They assume you're just wasting their time. You teach your players to be cynical and incurious and to engage with your work as little as possible, because in their experience it's mostly meaningless.For this reason, I consider the integration of chatbots into video games to fundamentally anti-artistic. It actively pushes players away from the work such that they get less out of it. Nobody wants this, actually. It sucks.  
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| ![](s.gif)|  [](vote?id=40325483&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325483) | root | parent | next [[–]](javascript:void\(0\))   
> A player can waste a literally infinite amount of time probing a chatbot NPC for narratively interesting details, and all they'll get is chatbot slop.I would phrase that as "A player can have an infinite amount of fun creating an immersive world with the LLM". Any details you want will be narratively interesting, because the LLM will work them into the narrative as necessary.There's no end of the hallway, the hallway leads to everything. The ultimate sandbox game.  
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| ![](s.gif)|  [](vote?id=40327345&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40327345) | root | parent | next [[–]](javascript:void\(0\))   
The ultimate sandbox is way less fun than a nicely curated sandbox  
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| ![](s.gif)|  [](vote?id=40327771&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40327771) | root | parent | next [[–]](javascript:void\(0\))   
For you. Minecraft wouldn't be a huge success if everyone shared your taste  
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| ![](s.gif)|  [](vote?id=40329395&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40329395) | root | parent | next [[–]](javascript:void\(0\))   
Minecraft doesn't have an LLM in it.Here's why Minecraft works: It's an ecosystem full of carefully-designed areas and resources (biomes fine-tuned to generate specific types of landscape, enemies with focus-tested behaviours, items with particular abilities) which combine to form a sandbox where players can exercise ingenuity and build projects that satisfy their creative vision.Where does generative AI fit into this? Should it be generating an infinite variety of dull and unoriginal items that don't do anything cool? Should it be building derivative-looking houses for players to ignore or burn down so that they can build their own houses in their place?LLM-generated slop has no place in games. It is an obstacle to having a good time.  
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| ![](s.gif)|  [](vote?id=40331569&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40331569) | root | parent | next [[–]](javascript:void\(0\))   
Before Minecraft came out, you'd be complaining about "Procedurally-generated slop", and saying there's no way a game like that would work.LLMs won't generate items or houses (or at least be the primary source). They'll give the world agency, in a way that's straight up impossible now.Like, maybe you screw someone over and they dedicate their lives to destroying you. Maybe you're just running around doing your thing and discover that the world has become embroiled in war all on their own. You can choose to join and fight, or play both sides, or just ignore it and keep adventuring. Maybe you're tired of adventuring and open up a flower shop, working your way up to creating a ruthless floral version of Standard Oil. Maybe you decide to kill the king and take his place, playing a game of cut-throat politics where people are constantly trying to backstab you. Then you decide, "Hey, I want to play as this other character and backstab my old self" and the LLM takes over your old character as you gleefully kill them off.If none of that sounds interesting to you, then there's lots of other games to play and I'm sure you'll have fun. Those are all things that I can't wait to see happen.  
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| ![](s.gif)|  [](vote?id=40343669&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40343669) | root | parent | next [[–]](javascript:void\(0\))   
> Before Minecraft came out, you'd be complaining about "Procedurally-generated slop"Before Minecraft came out, I played Nethack. No, I was never principally opposed to procgen games. However, there is such a thing as procedurally-generated slop. Good procgen games use a very fine-tuned algorithm to create engaging environments. Minecraft caves aren't fun to explore by accident; the algorithm was designed and tuned to consistently produce novel and fun layouts. Items and mobs were designed to specifically interact with those environments to lead to fun gameplay.You're missing the insight that games featuring procedural generation are still _designed._ Bad design will lead to a bad game where the algorithm produces unfun gameplay. LLMs are not independently capable of good design. Games where LLMs are given agency over the player's experience won't be fun.> maybe you screw someone over and they dedicate their lives to destroying youAnd maybe it's really annoying and unpleasant, because the game is doing a bad job of telling that story. You're assuming it'll be good, but LLMs aren't smart enough to tell stories well or to understand what kind of gameplay will be enjoyable.  
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| ![](s.gif)|  [](vote?id=40345761&how=up&goto=item%3Fid%3D40315434) |  [opyate](user?id=opyate) [10 months ago](item?id=40345761) | root | parent | next [[–]](javascript:void\(0\))   
> You're missing the insight that games featuring procedural generation are still designed.Best example of this is Spelunky, and this book goes into good detail: [https://bossfightbooks.com/products/spelunky-by-derek-yu](https://bossfightbooks.com/products/spelunky-by-derek-yu)Gist is that Derek designed these playable mini rooms which were ultimately randomly stitched together.  
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| ![](s.gif)|  [](vote?id=40331686&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40331686) | root | parent | prev | next [[–]](javascript:void\(0\))   
> Before Minecraft came out, you'd be complaining about "Procedurally-generated slop", and saying there's no way a game like that would work.People absolutely were complaining about this, with basically the same argument ("It's too much freedom! Players will get lost! They'll never get to the "goal" the artist sets!" etc)Of course, that turned out to be silly  
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| ![](s.gif)|  [](vote?id=40328646&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40328646) | root | parent | prev | next [[–]](javascript:void\(0\))   
Minecraft has extensive structure and curation to its experience.It’s main draw is also typically sharing YOUR creations with other people.  
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| ![](s.gif)|  [](vote?id=40331446&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40331446) | root | parent | next [[–]](javascript:void\(0\))   
The main draw for LLM-powered sandbox games will be sharing YOUR stories with other people.There will also be extensive structure and curation to these games. It will just be elements that you can play with as you see fit, much like Minecraft.  
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| ![](s.gif)|  [](vote?id=40332159&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40332159) | root | parent | next [[–]](javascript:void\(0\))   
Your examples of LLM integration is wildly inconsistent across your comments. I don’t believe you have an actual vision.  
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| ![](s.gif)|  [](vote?id=40332341&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40332341) | root | parent | next [[–]](javascript:void\(0\))   
I think you're confused about curation and unlimited freedom being possible? Those are not at odds with each other. Game devs will provide a world that they enjoy creating stories in. They'll playtest characters and write their backstories and whatnot. Then they'll hand that curated world over to the players. The players are then free to experience the world "as intended", or create their own new ways of making stories in that world.  
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| ![](s.gif)|  [](vote?id=40329350&how=up&goto=item%3Fid%3D40315434) |  [bccdee](user?id=bccdee) [10 months ago](item?id=40329350) | root | parent | prev | next [[–]](javascript:void\(0\))   
LLMs are boring, though. Have you actually tried going to ChatGPT and asking it to tell you a story? It isn't entertaining. It isn't interesting. It's a waste of time.Anything interesting will have to be provided by the player (as in a sandbox game) or by the designer (as in a conventional game), and at that point you've just ruined a perfectly good game by introducing an LLM to slather boring slop all over the interesting stuff the player or the designer has tried to do.  
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| ![](s.gif)|  [](vote?id=40323793&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40323793) | root | parent | prev | next [[–]](javascript:void\(0\))   
I disagree that scripted NPCs have nothing to say. You have a few basic types:* NPCs that don’t want to speak and usually offer a dismissive comment or a single repeating phrase. Fine candidates to LLM so long as this pattern doesn’t change. But also, who really cares?* important NPCs with specific written dialogue. Don’t even THINK about gambling with your story’s self consistency by giving these guys agency* the limited townsfolk type that has a small bit of something say. Like a typical Baldurs gate NPC. These people are fun to talk to because they “might” have something relevant or fun to say. Usually you don’t have to say anything. They just want to share things with you. It would be very tedious to chat with these people if they were infinite wells of lore and nothingness to be probed with written language.  
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| ![](s.gif)|  [](vote?id=40325507&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325507) | root | parent | next [[–]](javascript:void\(0\))   
I think what you're not getting is that there won't be a "your story". The player will create their own story in cooperation with the LLM. It won't be for every game, but think about games like Skyrim or BotW, where people ignore the main quest and have tons of fun doing whatever the hell they want.Game devs will still provide structure, "this big baddie is trying to do X, there's this mysterious treasure over here". But the guard rails will be gone, and there will be a real world to explore, not just cutscenes to sit through.  
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| ![](s.gif)|  [](vote?id=40327372&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40327372) | root | parent | next [[–]](javascript:void\(0\))   
BotW is an intensely well designed world with careful level planning.You can’t unlock the guard rails without making the general story completely arbitrary. It’s just not possible.  
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| ![](s.gif)|  [](vote?id=40327956&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40327956) | root | parent | next [[–]](javascript:void\(0\))   
> general story completely arbitraryThat's the exact promise. BotW has a main story, but it's not where the real fun is. The game devs will still write some overarching plot for people that want that, but you can just ignore that and live your own story.There will also be a lot of fun in seeing how far you can stretch the original story to its breaking point, like how people enjoy breaking Skyrim with too many mods. Testing the limits is the fun. "What if I defeated Ganon by taming an army of crabs?"  
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| ![](s.gif)|  [](vote?id=40328681&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40328681) | root | parent | next [[–]](javascript:void\(0\))   
Breaking games with shitty mods can be fun in the sense of dicking around but it quickly gets boring and frankly seems completely unrelated to trying to make actual good experiences with an LLM.BotW’s appeal is not the story. It’s definitely not the dialogue. It’s about exploring a carefully planned world full of goodies. Not a shitty LLM procedurally generated one that goes forever. And DEFINITELY not a run time generated one.  
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| ![](s.gif)|  [](vote?id=40331638&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40331638) | root | parent | next [[–]](javascript:void\(0\))   
BotW suffers for the NPCs having no agency or dimension to them. Why can't I infiltrate the Yiga clan and bend them to my will to help me defeat Ganon? Why can't I teach the villagers in TotK how to fight and fend for themselves against the pirates? There's a whole world that just needs a spark of agency to really live.  
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| ![](s.gif)|  [](vote?id=40332184&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40332184) | root | parent | next [[–]](javascript:void\(0\))   
I’m honestly just mad listening to these dumb fuck takes. I’m done. I’m tilted.This is such a stupidly thought out vision. BotW is a beautiful game that emphasizes minimalism and simplicity and you’re wanting to cram shit like this in.You can’t do those things because that’s not what the game is about. And if you could do those things, it would be a different fucking game, and the magicalness of the world and the ingenuity of its game mechanics would become nothing compared to your “limitless player narrative agency”I think your takes suck and have no basis in the reality of plausible to implement technology but this one really crosses the line of just being irritating to imagine asking for. This is the polar opposite of BotW’s design thesis and I hate you for tainting it.  
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| ![](s.gif)|  [](vote?id=40333436&how=up&goto=item%3Fid%3D40315434) |  [ux-app](user?id=ux-app) [10 months ago](item?id=40333436) | root | parent | next [[–]](javascript:void\(0\))   
>This is the polar opposite of BotW’s design thesis and I hate you for tainting it.LOL, calm down mate. Fwiw, I've read your back and forth with the GP and I'm much more excited with their vision than yours.Anyone with any imagination can see that LLMs are the future of story telling.You can rage all you want, but progress will march on without you.  
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| ![](s.gif)|  [](vote?id=40332935&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40332935) | root | parent | prev | next [[–]](javascript:void\(0\))   
Well that didn't take long.  
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| ![](s.gif)|  [](vote?id=40320351&how=up&goto=item%3Fid%3D40315434) |  [martindbp](user?id=martindbp) [10 months ago](item?id=40320351) | root | parent | prev | next [[–]](javascript:void\(0\))   
LLMs need to either be extremely good with insane context or LLM output has to be conditioned by and translated into formal logic, like Prolog or Datalog to make sure it's compatible with the rules and state of the world, and determine which effect player and NPC actions have on the world.  
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| ![](s.gif)|  [](vote?id=40325519&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325519) | root | parent | next [[–]](javascript:void\(0\))   
Longer context lengths will massively help, but game engines will also provide structure to the LLM. If you instruct an NPC to pick up a weapon and there isn't any, the engine will return an error to the LLM saying there's no weapon, and the LLM will write the character saying "What do you mean? There's no weapon."  
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| ![](s.gif)|  [](vote?id=40325765&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40325765) | root | parent | next [[–]](javascript:void\(0\))   
Another reason extremely long context lengths may not be fully necessary is that engines can store information in a database, update it based on further interactions, etc  
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| ![](s.gif)|  [](vote?id=40321169&how=up&goto=item%3Fid%3D40315434) |  [marcosdumay](user?id=marcosdumay) [10 months ago](item?id=40321169) | root | parent | prev | next [[–]](javascript:void\(0\))   
Logical conversation trees driving the LLM context seem way more reasonable to me than the other way around.But then you are adding a component of "how can I interrogate the NPC?" to the game. Anything you add to a game makes it a different game, and not necessarily a better one.  
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| ![](s.gif)|  [](vote?id=40320924&how=up&goto=item%3Fid%3D40315434) |  [mattbee](user?id=mattbee) [10 months ago](item?id=40320924) | root | parent | prev | next [[–]](javascript:void\(0\))   
"Ahoy there, one-armed pirate captain, couldst thou tell me how to implement the sieve of Eratosthenes in Python?"  
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| ![](s.gif)|  [](vote?id=40325063&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325063) | root | parent | prev | next [[–]](javascript:void\(0\))   
Odd comment. NPCs inventing details for themselves is the draw.NPCs will be limited by the game requiring the LLM to generate an action ("Move 5 units forward"), and then the game checks that it's a valid action, and rejects invalid ones.If you don't like metagaming LLMs, don't do that, and enjoy a regular roleplaying experienceNPCs won't have irrelevant backstories. The backstories generated on the fly for them by an LLM will be incorporated into the overall story.The overall story climax will happen based on player actions, indicating they're ready for the climax, based on the world created by the player's actions and specific instructions.I think you're trying to cram the square peg of limitless, custom narration into the round hole of a premade story. You can experience a decent roleplaying session already with an LLM you can run on your computer. Compute is only going to get cheaper, and LLM capabilities will only increase.  
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| ![](s.gif)|  [](vote?id=40327414&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40327414) | root | parent | next [[–]](javascript:void\(0\))   
> The overall story climax will happen based on player actions, indicating they're ready for the climax, based on the world created by the player's actions and specific instructions.Which means it’ll be a shit story because the world will not be robust enough to capture the infinite edge cases you’re pursuing.Current LLM DMing is rancid garbage. I’m assuming it gets several iterations better than the current state for us to even begin having this conversation  
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| ![](s.gif)|  [](vote?id=40327877&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40327877) | root | parent | next [[–]](javascript:void\(0\))   
potato/potato. It'll be a great story because it builds on your actual gameplay. It's not infinite edge cases, it's infinite ways to have your actions matter, instead of suffering through some hack writer's idea of a good story.> I’m assuming it gets several iterations better than the current stateYeah, that's the thing. LLMs are only going to improve at this. If you hate all LLM roleplaying everywhere, just wait a few years. Other people will have their own fun with them in the meantime  
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| ![](s.gif)|  [](vote?id=40328686&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40328686) | root | parent | next [[–]](javascript:void\(0\))   
Guess we’ll see. But I’m pretty sure you’re hugely incorrect.  
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| ![](s.gif)|  [](vote?id=40324144&how=up&goto=item%3Fid%3D40315434) |  [YeGoblynQueenne](user?id=YeGoblynQueenne) [10 months ago](item?id=40324144) | root | parent | prev | next [[–]](javascript:void\(0\))   
That's all as true as the fact there's a gun over the mantelpiece, at least if you're making Murder on the Orient Express or, you know, Horror on the Orient Express, and I'm all for some good old railroading with Victorian lampshades but I suspect that the designers interested in LLMs-as-NPCs are interested in some kind of open-world, immersive experience where PCs get to poke and prod every inch of the game world.The truth is that most people are used to games as these formulaic simulations with rigid rules. That's partly making a virtue out of necessity (very hard to play a game without rules; and some of us just like rules), and partly habit. Maybe LLMs can act as something new in that sense.Bang bang.  
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| ![](s.gif)|  [](vote?id=40326123&how=up&goto=item%3Fid%3D40315434) |  [dragonwriter](user?id=dragonwriter) [10 months ago](item?id=40326123) | root | parent | prev | next [[–]](javascript:void\(0\))   
Games with substantial roles for LLMs may be more like human GMed TTRPGs and less like traditional static-structured computer games, with less fixed narratives.  
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| ![](s.gif)|  [](vote?id=40315941&how=up&goto=item%3Fid%3D40315434) |  [radarsat1](user?id=radarsat1) [10 months ago](item?id=40315941) | parent | prev | next [[–]](javascript:void\(0\))   
I would have thought so too, until about 2023.But, if recent attitudes to AI are anything to go by, this will be immediately seen as boring and derivative at best, or at worst people will accuse the back stories as having been "stolen" from other works of fiction.While my expectations for the capabilities of AI have never been higher, my expectations for how the public will perceive this amazing technology has never been lower. It's all very disappointing.And that's without specifically placing blame by the way, perhaps it's big techs fault for going about this the wrong way, I don't know, but the public blowback against AI is just making working in this field that I used to love super depressing.  
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| ![](s.gif)|  [](vote?id=40316897&how=up&goto=item%3Fid%3D40315434) |  [guitarlimeo](user?id=guitarlimeo) [10 months ago](item?id=40316897) | root | parent | next [[–]](javascript:void\(0\))   
My 2 cents on why I think at the moment that I would find AI-driven games boring in the future (to clear confusion I think games are art):\- Amazing art is rare. Finding this amazing art nowadays is hard, with AI generated mumbojumbo increasing the noise it will be harder. It will be easier to just skip all AI generated stuff to find the real good stuff.\- Art has human touch or a message almost always in it. Purely AI generated stuff lacks meaning, because there's no creator.\- Socially sharing experiences that are unique only to you will be hard. I like to talk with my friends about the newest Nolan film or about Elden Ring's best bosses. If the whole game experience would be unique to me I wouldn't be able to relate with my friends at all and it would be the same as playing a different game.\- Valuing human work. I idolize people who have the skills to make great art. AI takes away from it.But to these points, I can also see the future where\- AI could generate amazing art a lot, and would make me reconsider my categorical exclusion of AI art.\- AI could make it seem there's meaning behind the art, or an artist would use the AI as a tool to convey his meaning. \- Unique game experiences would be so wild that you would be sharing them and relating via that way.\- AI becomes so common that valuing human skills isn't important anymoreLast point, I think novelty is something I value a lot and that's why in my opinion amazing art is rare. If AI would start creating a lot of novel art, novelty and rarity would just become common, and common is boring.  
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| ![](s.gif)|  [](vote?id=40321427&how=up&goto=item%3Fid%3D40315434) |  [Kim_Bruning](user?id=Kim_Bruning) [10 months ago](item?id=40321427) | root | parent | next [[–]](javascript:void\(0\))   
Shirley Bassey and the Propellorheads got it right: It's All Just A Little Bit Of History Repeating. [1]Writing[2], Print, Photography, Movies, Electronic music, Synths, Sampling, Trackers, Digital Photography, Photoshop, Ray Tracing, Interactive Art (eg computer games) etc etc ...Every time "it's not really art" , "there's no human touch".Every time, after a while artists learned to use it.And they made amazing art![1] [https://www.youtube.com/watch?v=yzLT6_TQmq8](https://www.youtube.com/watch?v=yzLT6_TQmq8) Propellerheads feat: Miss Shirley Bassey - History Repeating[2] Surely I'm kidding about the writing, right? [https://fs.blog/an-old-argument-against-writing/](https://fs.blog/an-old-argument-against-writing/)  
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| ![](s.gif)|  [](vote?id=40317643&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40317643) | root | parent | prev | next [[–]](javascript:void\(0\))   
> an artist would use the AI as a tool to convey his meaning.I'm not sure why this is shoved in as an aside near the end of the comment. This is how it's used. This is how it'll be used to create games.Does anyone think that, what, ChatGPT is going to poop out an entire finished game and we'll release that? Or would artists use AI as just another tool to create their art?  
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| ![](s.gif)|  [](vote?id=40320400&how=up&goto=item%3Fid%3D40315434) |  [krainboltgreene](user?id=krainboltgreene) [10 months ago](item?id=40320400) | root | parent | next [[–]](javascript:void\(0\))   
> I'm not sure why this is shoved in as an aside near the end of the comment. This is how it's used. This is how it'll be used to create games.No one who is good at what they do wants to do this. Everyone who is bad at what they do is doing this. I don't know why tech people think they understand the art industry at all.  
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| ![](s.gif)|  [](vote?id=40322765&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40322765) | root | parent | next [[–]](javascript:void\(0\))   
I'm sorry, I forgot that tech people can't do art and therefore can't have an opinion on it.  
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| ![](s.gif)|  [](vote?id=40325343&how=up&goto=item%3Fid%3D40315434) |  [krainboltgreene](user?id=krainboltgreene) [10 months ago](item?id=40325343) | root | parent | next [[–]](javascript:void\(0\))   
man if only i used a specific word to give a narrow context to my statement oh well  
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| ![](s.gif)|  [](vote?id=40325809&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40325809) | root | parent | next [[–]](javascript:void\(0\))   
What context did I miss exactly that would change the fact that people can work in multiple industries at once, or can change industries over time, or the large overlap between "tech" and "art" these days?  
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| ![](s.gif)|  [](vote?id=40319531&how=up&goto=item%3Fid%3D40315434) |  [Workaccount2](user?id=Workaccount2) [10 months ago](item?id=40319531) | root | parent | prev | next [[–]](javascript:void\(0\))   
>Does anyone think that, what, ChatGPT is going to poop out an entire finished game and we'll release that?Well if this trajectory continues upwards, it almost certainly will happen in the next decade.Right now feels like how the 70's-80's felt with the rise of the microprocessor. Tons of promise and implementation ideas, but the tech just wasn't there yet. But sure as hell it came around.  
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| ![](s.gif)|  [](vote?id=40319341&how=up&goto=item%3Fid%3D40315434) |  [Chabsff](user?id=Chabsff) [10 months ago](item?id=40319341) | root | parent | prev | next [[–]](javascript:void\(0\))   
Unfortunately, creative and interesting use of the technology is both not how its being portrayed and also, critically, not how its being marketed and/or used.The current expectation is that these models allow the creation of _equivalent_ artwork to what artists are currently producing, but at larger scale / lower cost / shorter training. All of these boil down to less effort. And perceived effort is a large part of what brings value to art.  
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| ![](s.gif)|  [](vote?id=40316146&how=up&goto=item%3Fid%3D40315434) |  [antihipocrat](user?id=antihipocrat) [10 months ago](item?id=40316146) | parent | prev | next [[–]](javascript:void\(0\))   
Imagine having NPCs with prompt set character traits and set boundaries of knowledge of the game world.The player can interact freely with the NPC rather than through predetermined conversation decision trees, potentially driving a lot of unique emergent gameplay.Downsides are that it is a lot quicker just to skip through conversations with the decision tree format and having to think about and type in questions could get tiresome. Another downside is players jailbreaking the LLM...  
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| ![](s.gif)|  [](vote?id=40317106&how=up&goto=item%3Fid%3D40315434) |  [keyle](user?id=keyle) [10 months ago](item?id=40317106) | parent | prev | next [[–]](javascript:void\(0\))   
Yes, I look forward to not have the NPC repeat the same exact thing every time, as you pass them by.  
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| ![](s.gif)|  [](vote?id=40317144&how=up&goto=item%3Fid%3D40315434) |  [jsheard](user?id=jsheard) [10 months ago](item?id=40317144) | root | parent | next [[–]](javascript:void\(0\))   
How much framerate are you willing to give up so that NPCs don't repeat themselves? The elephant in the room with LLMs in games is that the users GPU is usually already being maxed out by pushing pixels, so any amount of compute allocated to running AI models instead is going to have a very tangible cost.They could be offloaded to the cloud instead but that brings its own set of issues (ongoing upkeep costs, finite capacity, servers will inevitably shut down...)  
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| ![](s.gif)|  [](vote?id=40317578&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40317578) | root | parent | next [[–]](javascript:void\(0\))   
> The elephant in the room ..._For now._ LLMs are still relatively new. The technology will continue to be optimized, and we'll be shipping tiny LLMs along with games soon enough.(Aren't companies already running LLMs on phones?)  
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| ![](s.gif)|  [](vote?id=40320422&how=up&goto=item%3Fid%3D40315434) |  [krainboltgreene](user?id=krainboltgreene) [10 months ago](item?id=40320422) | root | parent | next [[–]](javascript:void\(0\))   
The housing market will only go up, it's always gone up.  
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| ![](s.gif)|  [](vote?id=40319584&how=up&goto=item%3Fid%3D40315434) |  [Workaccount2](user?id=Workaccount2) [10 months ago](item?id=40319584) | root | parent | prev | next [[–]](javascript:void\(0\))   
I can only imagine nvidia/amd pouncing on the chance to sell companion "AI Cards" that enable LLM NPCs in games. If the tech is good (and insane progress in tiny local models points to "yes"), then I can easily imagine a large contingent of gamers forking over another $600 for a second GPU.It might not even be necessary though, similar to how physixs (sp?) cards died out quick in the early 00's, the models might become slim enough and the GPU's fast enough that the extra load is easily manageable.  
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| ![](s.gif)|  [](vote?id=40319661&how=up&goto=item%3Fid%3D40315434) |  [jsheard](user?id=jsheard) [10 months ago](item?id=40319661) | root | parent | next [[–]](javascript:void\(0\))   
The vast majority of games need to run on consoles to be financially viable, anything that requires an entire second GPU is a complete non-starter. The only way it's going to work is if the models get small enough to run on a negligible slice of GPU or CPU time, with a reasonable memory footprint (<1GB), on whatever the current console hardware happens to be.  
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| ![](s.gif)|  [](vote?id=40319782&how=up&goto=item%3Fid%3D40315434) |  [Workaccount2](user?id=Workaccount2) [10 months ago](item?id=40319782) | root | parent | next [[–]](javascript:void\(0\))   
I see your point, but it hasn't really stopped novel features from coming to PC in the past. The PC market is about 50% larger than the console market too.  
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| ![](s.gif)|  [](vote?id=40325537&how=up&goto=item%3Fid%3D40315434) |  [gaganyaan](user?id=gaganyaan) [10 months ago](item?id=40325537) | root | parent | prev | next [[–]](javascript:void\(0\))   
Console manufacturers would salivate at the thought of new hardware that makes people want to buy a new console instead of playing the ones they've already got. Once someone gets it working reasonably on PC, it will quickly spread to a new generation of consoles with new hardware that makes people want to upgrade.  
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| ![](s.gif)|  [](vote?id=40317495&how=up&goto=item%3Fid%3D40315434) |  [YurgenJurgensen](user?id=YurgenJurgensen) [10 months ago](item?id=40317495) | root | parent | prev | next [[–]](javascript:void\(0\))   
Instead you'll just get variations on "Sorry, but as a large language model trained by OpenAl, I cannot condone the shooting of adventurers in the knees with arrows."  
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| ![](s.gif)|  [](vote?id=40318958&how=up&goto=item%3Fid%3D40315434) |  [kozikow](user?id=kozikow) [10 months ago](item?id=40318958) | root | parent | prev | next [[–]](javascript:void\(0\))   
Any production deployment would probably do pre-fetching of potential initial dialogues as some kind of background job.  
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| ![](s.gif)|  [](vote?id=40319029&how=up&goto=item%3Fid%3D40315434) |  [squigz](user?id=squigz) [10 months ago](item?id=40319029) | root | parent | next [[–]](javascript:void\(0\))   
On this note - and as a response to the point a commenter made that too much freedom in dialogue might actually be a bad thing - LLMs could then be used to expand on the initial dialogue in a way that feels more 'organic' every time you hear it, but is still close to the artists' vision.  
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| ![](s.gif)|  [](vote?id=40316808&how=up&goto=item%3Fid%3D40315434) |  [uoaei](user?id=uoaei) [10 months ago](item?id=40316808) | parent | prev | next [[–]](javascript:void\(0\))   
Now just imagine what we could do with a _purpose-built ML model_!!!  
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| ![](s.gif)|  [](vote?id=40318438&how=up&goto=item%3Fid%3D40315434) |  [ramcle](user?id=ramcle) [10 months ago](item?id=40318438) | parent | prev | next [[–]](javascript:void\(0\))   
It could also work well for modding, for example, creating a natural dialogue system in Morrowind (replacing the vanilla "interactive signpost" NPCs).  
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| ![](s.gif)|  [](vote?id=40316273&how=up&goto=item%3Fid%3D40315434) |  [delusional](user?id=delusional) [10 months ago](item?id=40316273) | prev | next [[–]](javascript:void\(0\))   
I don't believe this is going to actually make it into games I have any interest in playing. Ubisoft might include it for a bit in some awful games, but it will fizzle out quick.For the sake of argument, let's imagine it actually did make it into some game you'd want to play. It's well known that the quality of generation from these LLM's is heavily dependent on the quality of the text you put into it. Does that mean the quality of the game narrative is now going to be dependent on how I play it? Will normal people who just like to mess about get a worse game experience than the guy following the traffic laws in GTA? What's the creative talent even doing in that world?  
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| ![](s.gif)|  [](vote?id=40316355&how=up&goto=item%3Fid%3D40315434) |  [Terr_](user?id=Terr_) [10 months ago](item?id=40316355) | parent | next [[–]](javascript:void\(0\))   
In contrast to runtime stuff (like an AI dungeon master) I've long wished for _authoring tools_ to help people create rich game-worlds and quest interactions. On reflection, that's something I wouldn't expect LLMs to deliver either: It relies heavily on modeling cause-and-effect, with choices which are blocked or enabled by other choices.I'm talking about stuff that could throw up a warning like: "Warning: Broken quest steps in For The Want of a Nail. Invariant Character:Blacksmith may have been killed by 5 other paths."  
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| ![](s.gif)|  [](vote?id=40319728&how=up&goto=item%3Fid%3D40315434) |  [Workaccount2](user?id=Workaccount2) [10 months ago](item?id=40319728) | root | parent | next [[–]](javascript:void\(0\))   
I wonder if models trained on the real world will unintentionally destroy the game experience for people. In (NPC structured) video games things always work out ideally, you invest and you get a return. Any risk is illustrated for the user.With LLM training, the NPC might take a more realistic approach and inform you that 4 of 6 the beetle nuts you collected for them are cracked and useless, and next time you should be more careful packing them in your backpack.  
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| ![](s.gif)|  [](vote?id=40320446&how=up&goto=item%3Fid%3D40315434) |  [krainboltgreene](user?id=krainboltgreene) [10 months ago](item?id=40320446) | root | parent | prev | next [[–]](javascript:void\(0\))   
> authoring tools to help people create rich game-worlds and quest interactionsI built one of those things. It used random generation from datasets to create everything from gods and their pantheons to their followers, cities, and the cultures that make up the cities.None of it used LLMs and it created better content than any model could, but it was hard to debug and harder still to figure out "What even is a culture?"  
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| ![](s.gif)|  [](vote?id=40326107&how=up&goto=item%3Fid%3D40315434) |  [delusional](user?id=delusional) [10 months ago](item?id=40326107) | root | parent | prev | next [[–]](javascript:void\(0\))   
That's a more interesting usecase. But what you're describing is just an IDE, there's nothing AI about that.  
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| ![](s.gif)|  [](vote?id=40317616&how=up&goto=item%3Fid%3D40315434) |  [YurgenJurgensen](user?id=YurgenJurgensen) [10 months ago](item?id=40317616) | parent | prev | next [[–]](javascript:void\(0\))   
There's a bigger (but related) problem than text quality:With a usual game environment, the devs create a whitelist of actions the player is allowed to perform. If there's no combat, they simply don't give the player the option to perform violent actions. If they don't want the player to vault barriers they just don't include a jump button. Vehicles and buildings are props by default and only become intractable because the devs consciously chose to make them so. Devs have generally gotten very good at implying what's out of scope, so players generally don't even attempt actions that are intended to be impossible.In LLM world with free-text entry, controlling what's on-theme becomes a blacklist rather than a whitelist, as the player can choose to input anything. Including things for flavour or lore reasons now becomes more difficult, as a whitelist is finite, while the blacklist of undesired actions is practically infinite.  
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| ![](s.gif)|  [](vote?id=40321614&how=up&goto=item%3Fid%3D40315434) |  [delusional](user?id=delusional) [10 months ago](item?id=40321614) | root | parent | next [[–]](javascript:void\(0\))   
I don't quite buy that contrast. I agree as long as we are talking story. The developers will have a very difficult time trying to control what some character is like, and if that matters for the gameplay it's a big problem. On the gameplay front the available actions will always be a whitelist, because somebody has to implement them. It's essentially the same problem the crypto kids had with their "cross game non-fungible items", where they just assumed that items would somehow be portable. Just because you can get some NPC to tell you about a dragon in the nearby cave doesn't make that cave actually appear. You're never going to get to fight a darksouls boss in forza horizon. The game might tell you that you get to, that doesn't make it true.  
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| ![](s.gif)|  [](vote?id=40323127&how=up&goto=item%3Fid%3D40315434) |  [YurgenJurgensen](user?id=YurgenJurgensen) [10 months ago](item?id=40323127) | root | parent | next [[–]](javascript:void\(0\))   
That's still a problem though, right?If the NPCs are writing cheques that the engine can't cash, the player won't necessarily be able to trust any NPC dialogue.  
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| ![](s.gif)|  [](vote?id=40326093&how=up&goto=item%3Fid%3D40315434) |  [delusional](user?id=delusional) [10 months ago](item?id=40326093) | root | parent | next [[–]](javascript:void\(0\))   
Yes. You won't be able to trust what they say in relation to the story, not to the actuality of the gameplay. They're dialogue with essentially be orthogonal to play. Talk about ludo-narrative dissonance.  
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| ![](s.gif)|  [](vote?id=40315879&how=up&goto=item%3Fid%3D40315434) |  [upwardbound](user?id=upwardbound) [10 months ago](item?id=40315879) | prev | next [[–]](javascript:void\(0\))   
Something related is compellingly explored in an interesting trilogy of LitRPG comedy books called the Vaudevillain series, consisting of Top Hat Express, Black Tie Villainy, and Cane Whirling Lunacy. In the series, there is a VR video game called “World of Supers” where players can type in any superpower they can dream of into a game-master LLM, and the LLM will then create a character for them which has that power, but reworked a little bit to fit into game balance. For example, the player could type “I can stop time and still move around and take actions for up to 90 seconds” and the game-master LLM may approve this power but alter 90 seconds to 10 seconds, and give the player a major weakness, such as “However, stopping time in the vicinity of a gamma ray source will result in immediate loss of 75% of HP due to the interaction of the time stop with the radiation’s effects. This HP loss can be mitigated if you are encased in a significant amount of lead, such as a lead vehicle or heavy lead armor.” It's a pretty neat concept for how LLMs could allow custom player abilities while maintaining game balance. That way, player agency extends to actual powers/abilities, not just dialogue.  
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| ![](s.gif)|  [](vote?id=40316038&how=up&goto=item%3Fid%3D40315434) |  [idle_zealot](user?id=idle_zealot) [10 months ago](item?id=40316038) | parent | next [[–]](javascript:void\(0\))   
That would of course require an LLM to _understand_ game balance, as opposed to just writing things that sound like they might balance a hypothetical game. At that point it's less similar to our LLMs and more like an AGI. And at that point we may as well have it create the neurotech necessary for the sort of VR tech I assume is used in the referenced books' world.  
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| ![](s.gif)|  [](vote?id=40316060&how=up&goto=item%3Fid%3D40315434) |  [upwardbound](user?id=upwardbound) [10 months ago](item?id=40316060) | root | parent | next [[–]](javascript:void\(0\))   
Agree. This point is a big concern and is explored in the stories (the protagonist creates a "mad scientist" character that can use the LLM to invent new equipment, and then uses a lot of trial and error to find prompts that generate insanely overpowered equipment). It sounds fun to play though! Balance doesn't have to be .. well .. balanced if there are other ways to maintain fun, such as leaning heavily into the creativity & self-actualization angle. As an example, if I could have any superpower in real life, I'd love to be able to fly, even though that's actually a very weak power compared to things like teleporting that are mostly a superset of flying, but not as fun and whimsical.Encouraging "living your dream" rather than being the most powerful might be easier to achieve in a game with coop or open-world objectives rather than one focused on PvP, but if "living your dream" could be compelling made into the primary player objective, it would pair very well with the custom powers mechanic envisioned in the story.As an example of how this is explored in the story, one of the protagonist's friends who loves surfing has a character whose superpower is "the ability to surf _anything_ ". It's an extremely weak power but the character really loves it because they get to surf the most extreme things imaginable, just for the fun of it, including in outlandish scenarios such as surfing along the blast wave of an explosion, or even the beam of a laser - silly stuff that is fun to explore & try.I think it wouldn't have to be VR to be fun - imagine if it was something like a Roblox-based game played on laptops. Even then, the "surf anything" power and other outlandish powers would be incredibly fun to use. Imagine if you could do almost anything! You don't have to be the most powerful player in the world to have fun. Maybe you just _really_ want to pilot a mecha-Godzilla, or something - even though an Iron Man or Thor level super could smash it in seconds. It's still awesome!  
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| ![](s.gif)|  [](vote?id=40316189&how=up&goto=item%3Fid%3D40315434) |  [throwaway71271](user?id=throwaway71271) [10 months ago](item?id=40316189) | prev | next [[–]](javascript:void\(0\))   
Finally we can have interesting games with real meaningful choices that actually change the narrative of the game. Modern MMORPGs are barely better than the Choose Your Own Adventure books.Every time I watch Chris Crawford's Dragon Speech: [https://www.youtube.com/watch?v=VwZi58u1FjI](https://www.youtube.com/watch?v=VwZi58u1FjI) I think about what could've been, and what games actually turn out to be.Honestly, if the next The War Within (next World of Warcraft expansion) makes me do 10 daily quests per day again, I will cancel my subscription for the first time since 2005.  
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| ![](s.gif)|  [](vote?id=40316308&how=up&goto=item%3Fid%3D40315434) |  [c048](user?id=c048) [10 months ago](item?id=40316308) | parent | next [[–]](javascript:void\(0\))   
You need focus and direction in any media. Books, movies, games, etc.... they all need it.The first NPC that you encounter with such freedom of interaction is interesting, the 45th will be annoying and you'll just want to get to the point. The holodeck of Star Trek is often brought up as an excellent example of AI generated interactions, but people seem to be forgetting that the stories we see unfold on TV aren't random interactive stories. They're scripted stories written by show's writers.Unless you want to make a simulation without a real goal, I don't see how this could lead to fun and interesting gameplay past the first hour. Realism is, always will be, a bad gameplay pillar just for the sake of it.  
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| ![](s.gif)|  [](vote?id=40316426&how=up&goto=item%3Fid%3D40315434) |  [throwaway71271](user?id=throwaway71271) [10 months ago](item?id=40316426) | root | parent | next [[–]](javascript:void\(0\))   
There is a group of orc kids running around in Orgrimmar, and sometimes they run around you and are very annoying, why can't I just trip one.I had a friend who didn't want to fight against the Lich King. When we finally got to him in the raid, at the start of the fight he kneeled in front of him and died (caused us unnecessary wipe, but.. was cool).I am not thinking about 'AI generated interactions', and I think the AI can create compelling story that you go through. Kind of like in Sword Art Online or Shangri-La Frontier's quests.  
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| ![](s.gif)|  [](vote?id=40316768&how=up&goto=item%3Fid%3D40315434) |  [guitarlimeo](user?id=guitarlimeo) [10 months ago](item?id=40316768) | root | parent | next [[–]](javascript:void\(0\))   
It surely can generate something better than the old "Fetch this item and come back" quests, but I wouldn't say the possibilities are endless, so eventually you would get bored of the same repeating AI quests the same way as you do now.Good storytelling is hard and AI is not some magic bullet that can just solve it for good. But it can help raise the floor of the unimportant side quests.  
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| ![](s.gif)|  [](vote?id=40320486&how=up&goto=item%3Fid%3D40315434) |  [krainboltgreene](user?id=krainboltgreene) [10 months ago](item?id=40320486) | root | parent | prev | next [[–]](javascript:void\(0\))   
> why can't I just trip one.Some of the best writing and experiences I've ever had gaming have never come from the ability to do random acts that are worthwhile to noone.> I had a friend who didn't want to fight against the Lich King. When we finally got to him in the raid, at the start of the fight he kneeled in front of him and died (caused us unnecessary wipe, but.. was cool).Man that sounds cringe, not cool.  
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| ![](s.gif)|  [](vote?id=40319870&how=up&goto=item%3Fid%3D40315434) |  [PeterisP](user?id=PeterisP) [10 months ago](item?id=40319870) | parent | prev | next [[–]](javascript:void\(0\))   
As my kids got some recent Choose Your Own Adventure books, I'd say that the quests in modern MMORPGs tend to be significantly worse than that.  
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| ![](s.gif)|  [](vote?id=40318411&how=up&goto=item%3Fid%3D40315434) |  [nsagent](user?id=nsagent) [10 months ago](item?id=40318411) | prev | next [[–]](javascript:void\(0\))   
I've also done research in the use of LLMs for RPGs, using Disco Elysium as a testbed (rather than the small made up scenario seen in this paper).[https://aclanthology.org/2023.findings-emnlp.151/](https://aclanthology.org/2023.findings-emnlp.151/)[https://pl.aiwright.dev/](https://pl.aiwright.dev/)  
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| ![](s.gif)|  [](vote?id=40316233&how=up&goto=item%3Fid%3D40315434) |  [chilldsgn](user?id=chilldsgn) [10 months ago](item?id=40316233) | prev | next [[–]](javascript:void\(0\))   
Ah, my friend created guides for soloing (or play with a friend without a GM) TTRPGs using ChatGPT, here's his site: [https://oracle-rpg.com/](https://oracle-rpg.com/)The Discord is pretty active too.I forwarded him this study, he'll appreciate it!  
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| ![](s.gif)|  [](vote?id=40318169&how=up&goto=item%3Fid%3D40315434) |  [Bjartr](user?id=Bjartr) [10 months ago](item?id=40318169) | prev | next [[–]](javascript:void\(0\))   
The criticisms people are laying out about being able to say anything making games boring would apply to D&D as well. Since D&D is really fun despite the freedom to (attempt) to say or do whatever your imagination can dream up means it's at least plausible that a game with generative dialogue and narrative could work well.Just because you're free to speak doesn't mean NPCs will listen, nor does it mean there aren't rules or that there won't be consequences.  
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| ![](s.gif)|  [](vote?id=40320206&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40320206) | parent | next [[–]](javascript:void\(0\))   
D&D is fun because it is a social experience. There’s also a level of expected player etiquette. If you want to go off the rails and completely ignore the party and plot, you’ll probably be kicked out of the group. D&D is fun, but the vast majority of campaigns are cut short and unfinished.A game with generative dialogue could work well. But one should expect that slapping it into most games will work horribly.  
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| ![](s.gif)|  [](vote?id=40331344&how=up&goto=item%3Fid%3D40315434) |  [Bjartr](user?id=Bjartr) [10 months ago](item?id=40331344) | root | parent | next [[–]](javascript:void\(0\))   
Slapping any mechanic in without thought rarely produces a good game.> If you want to go off the rails and completely ignore the party and plot, you’ll probably be kicked out of the group.Sounds like a great place an AI can fill in as is DM for otherwise toxic players.  
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| ![](s.gif)|  [](vote?id=40315678&how=up&goto=item%3Fid%3D40315434) |  [doctorpangloss](user?id=doctorpangloss) [10 months ago](item?id=40315678) | prev | next [[–]](javascript:void\(0\))   
The real bummer is that if you want to get financing to make a game right now, putting a PDF onto arxiv.org will get you closer to that goal than actually making a game.  
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| ![](s.gif)|  [](vote?id=40316480&how=up&goto=item%3Fid%3D40315434) |  [unixhero](user?id=unixhero) [10 months ago](item?id=40316480) | prev | next [[–]](javascript:void\(0\))   
I have not sat down and thought through how LLMs would be made use of in the World of Baldurs Gate 3. But when I do, my mind will be boggled.It will surely also mean always online games with a hefty subscription fee.Unless!!! The llm can run locally on the player gpu... Whoa mind boggled. This is coming, I'm sure of it./end internal thought process  
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| ![](s.gif)|  [](vote?id=40316511&how=up&goto=item%3Fid%3D40315434) |  [benreesman](user?id=benreesman) [10 months ago](item?id=40316511) | parent | next [[–]](javascript:void\(0\))   
All LLMs in that regime can be run on mid-range gaming hardware _today_.LLMs run off-prem/off-desk are already niche for enthusiasts. You need Opus or 0125 _sometimes_ , but not often.Apple just announced their intention to make running them in the cloud niche generally, at least for mundane consumer use cases, work out of the box.  
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| ![](s.gif)|  [](vote?id=40320267&how=up&goto=item%3Fid%3D40315434) |  [jncfhnb](user?id=jncfhnb) [10 months ago](item?id=40320267) | parent | prev | next [[–]](javascript:void\(0\))   
Can you write a single line of dialogue that you think would make the game better?  
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| ![](s.gif)|  [](vote?id=40316543&how=up&goto=item%3Fid%3D40315434) |  [robblbobbl](user?id=robblbobbl) [10 months ago](item?id=40316543) | prev | next [[–]](javascript:void\(0\))   
Good job. More LLMs in Games please!  
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| ![](s.gif)|  [](vote?id=40321229&how=up&goto=item%3Fid%3D40315434) |  [WhiteNoiz3](user?id=WhiteNoiz3) [10 months ago](item?id=40321229) | prev | next [[–]](javascript:void\(0\))   
A lot of the dialogue that OpenAI's models write is incredibly bland.. I really think we'll need less censored models trained on how to act different roles other than just 'super safe and friendly assistant'.  
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| ![](s.gif)|  [](vote?id=40317919&how=up&goto=item%3Fid%3D40315434) |  [j-bos](user?id=j-bos) [10 months ago](item?id=40317919) | prev | next [[–]](javascript:void\(0\))   
Not as in depth as the abstract, but I found this to be a well integrated use of LLM's into a game: [https://youtu.be/sUL0Z7PwanY](https://youtu.be/sUL0Z7PwanY)  
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| ![](s.gif)|  [](vote?id=40315699&how=up&goto=item%3Fid%3D40315434) |  [flawsofar](user?id=flawsofar) [10 months ago](item?id=40315699) | prev [[–]](javascript:void\(0\))   
Hey, this currently has a bug but I want to share here.The framework should be clear.I’ve written a React pattern for writing interactive fiction using LLMs.Here’s a demo with Taskmaster roleplay implemented.[https://github.com/LEXNY/Taskmaster-LLM/blob/main/main.js](https://github.com/LEXNY/Taskmaster-LLM/blob/main/main.js)  
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