# documents

url: https://arxiv.org/html/2310.14735v3
title: Unleashing the potential of prompt engineering: a comprehensive review
scrape_date: 2025-03-17T13:28:36.202983
content_type: markdown
hash: b79116786af6153a1da5b013d5e53f3e21ca13754891f8cd0d79a9d746e7a756
content:
  1. [1 Introduction](https://arxiv.org/html/2310.14735v3#S1 "In Unleashing the potential of prompt engineering: a comprehensive review")
  2. [2 Basics of prompt engineering](https://arxiv.org/html/2310.14735v3#S2 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [2.1 Model introduction: GPT-4](https://arxiv.org/html/2310.14735v3#S2.SS1 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [2.2 Giving instructions](https://arxiv.org/html/2310.14735v3#S2.SS2 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    3. [2.3 Be clear and precise](https://arxiv.org/html/2310.14735v3#S2.SS3 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    4. [2.4 Role-prompting](https://arxiv.org/html/2310.14735v3#S2.SS4 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    5. [2.5 Use of triple quotes to separate](https://arxiv.org/html/2310.14735v3#S2.SS5 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    6. [2.6 Try several times](https://arxiv.org/html/2310.14735v3#S2.SS6 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    7. [2.7 One-shot or few-shot prompting](https://arxiv.org/html/2310.14735v3#S2.SS7 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    8. [2.8 LLM settings: temperature and top-p](https://arxiv.org/html/2310.14735v3#S2.SS8 "In 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  3. [3 Advanced methodologies](https://arxiv.org/html/2310.14735v3#S3 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [3.1 Chain of thought](https://arxiv.org/html/2310.14735v3#S3.SS1 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      1. [3.1.1 Zero-shot chain of thought](https://arxiv.org/html/2310.14735v3#S3.SS1.SSS1 "In 3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      2. [3.1.2 Golden chain of thought](https://arxiv.org/html/2310.14735v3#S3.SS1.SSS2 "In 3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [3.2 Self-consistency](https://arxiv.org/html/2310.14735v3#S3.SS2 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    3. [3.3 Generated knowledge](https://arxiv.org/html/2310.14735v3#S3.SS3 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    4. [3.4 Least-to-most prompting](https://arxiv.org/html/2310.14735v3#S3.SS4 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    5. [3.5 Tree of thoughts](https://arxiv.org/html/2310.14735v3#S3.SS5 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    6. [3.6 Graph of thoughts](https://arxiv.org/html/2310.14735v3#S3.SS6 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    7. [3.7 Automatic Reasoning and Tool Usage](https://arxiv.org/html/2310.14735v3#S3.SS7 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    8. [3.8 Active-Prompt](https://arxiv.org/html/2310.14735v3#S3.SS8 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    9. [3.9 ReAct Framework](https://arxiv.org/html/2310.14735v3#S3.SS9 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    10. [3.10 Retrieval augmentation](https://arxiv.org/html/2310.14735v3#S3.SS10 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    11. [3.11 Use plugins to polish the prompts](https://arxiv.org/html/2310.14735v3#S3.SS11 "In 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  4. [4 Methodologies of Multimodal LM](https://arxiv.org/html/2310.14735v3#S4 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [4.1 Multi-modal Prompt Learning (MaPLe)](https://arxiv.org/html/2310.14735v3#S4.SS1 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [4.2 Context Optimization](https://arxiv.org/html/2310.14735v3#S4.SS2 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    3. [4.3 Conditional Prompt Learning](https://arxiv.org/html/2310.14735v3#S4.SS3 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    4. [4.4 Modular Prompting](https://arxiv.org/html/2310.14735v3#S4.SS4 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    5. [4.5 Zero-shot and Few-shot Prompting](https://arxiv.org/html/2310.14735v3#S4.SS5 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    6. [4.6 Reinforcement Learning for Prompt Optimization](https://arxiv.org/html/2310.14735v3#S4.SS6 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    7. [4.7 Prompt Pattern Catalog](https://arxiv.org/html/2310.14735v3#S4.SS7 "In 4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  5. [5 Assessing the efficacy of prompt methods](https://arxiv.org/html/2310.14735v3#S5 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [5.1 Subjective and objective evaluations](https://arxiv.org/html/2310.14735v3#S5.SS1 "In 5 Assessing the efficacy of prompt methods ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [5.2 Comparing different prompt methods](https://arxiv.org/html/2310.14735v3#S5.SS2 "In 5 Assessing the efficacy of prompt methods ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  6. [6 Applications improved by prompt engineering](https://arxiv.org/html/2310.14735v3#S6 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [6.1 Assessment in teaching and learning](https://arxiv.org/html/2310.14735v3#S6.SS1 "In 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [6.2 Content creation and editing](https://arxiv.org/html/2310.14735v3#S6.SS2 "In 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    3. [6.3 Computer programming](https://arxiv.org/html/2310.14735v3#S6.SS3 "In 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    4. [6.4 Reasoning tasks](https://arxiv.org/html/2310.14735v3#S6.SS4 "In 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    5. [6.5 Dataset generation](https://arxiv.org/html/2310.14735v3#S6.SS5 "In 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  7. [7 Exploring and Enhancing LLMs through Insightful Prompt Engineering](https://arxiv.org/html/2310.14735v3#S7 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [7.1 Introduction to Security in LLMs](https://arxiv.org/html/2310.14735v3#S7.SS1 "In 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [7.2 Common Vulnerabilities in LLMs](https://arxiv.org/html/2310.14735v3#S7.SS2 "In 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      1. [7.2.1 Adversarial Attacks](https://arxiv.org/html/2310.14735v3#S7.SS2.SSS1 "In 7.2 Common Vulnerabilities in LLMs ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      2. [7.2.2 Prompt Hacking](https://arxiv.org/html/2310.14735v3#S7.SS2.SSS2 "In 7.2 Common Vulnerabilities in LLMs ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      3. [7.2.3 Threat of Model Stealing](https://arxiv.org/html/2310.14735v3#S7.SS2.SSS3 "In 7.2 Common Vulnerabilities in LLMs ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      4. [7.2.4 Backdoor Threats](https://arxiv.org/html/2310.14735v3#S7.SS2.SSS4 "In 7.2 Common Vulnerabilities in LLMs ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      5. [7.2.5 Data Poisoning](https://arxiv.org/html/2310.14735v3#S7.SS2.SSS5 "In 7.2 Common Vulnerabilities in LLMs ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    3. [7.3 Prompt Engineering as a Double-Edged Sword](https://arxiv.org/html/2310.14735v3#S7.SS3 "In 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      1. [7.3.1 Impact of Poorly Designed Prompts on Security](https://arxiv.org/html/2310.14735v3#S7.SS3.SSS1 "In 7.3 Prompt Engineering as a Double-Edged Sword ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
      2. [7.3.2 Enhancing LLM Security Through Adversarial Training and Robust Prompt Design](https://arxiv.org/html/2310.14735v3#S7.SS3.SSS2 "In 7.3 Prompt Engineering as a Double-Edged Sword ‣ 7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  8. [8 Prospective methodologies](https://arxiv.org/html/2310.14735v3#S8 "In Unleashing the potential of prompt engineering: a comprehensive review")
    1. [8.1 Better understanding of structures](https://arxiv.org/html/2310.14735v3#S8.SS1 "In 8 Prospective methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
    2. [8.2 Agent for AIGC tools](https://arxiv.org/html/2310.14735v3#S8.SS2 "In 8 Prospective methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")
  9. [9 Conclusion](https://arxiv.org/html/2310.14735v3#S9 "In Unleashing the potential of prompt engineering: a comprehensive review")
  10. [10 Acknowledgement](https://arxiv.org/html/2310.14735v3#S10 "In Unleashing the potential of prompt engineering: a comprehensive review")



[1]\fnmNicolas \surLangrené [2,1]\fnmShengxin \surZhu

1]\orgnameGuangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, \orgaddress\cityZhuhai \postcode519087, \countryChina 2]\orgnameResearch Center for Mathematics, Beijing Normal University, \orgaddress\streetNo.18, Jingfeng Road, \cityZhuhai \postcode519087, \stateGuangdong, \countryChina

# Unleashing the potential of prompt engineering: a comprehensive review

\fnmBanghao \surChen  [q030026007@mail.uic.edu.cn](mailto:q030026007@mail.uic.edu.cn) \fnmZhaofeng \surZhang  [q030018107@mail.uic.edu.cn](mailto:q030018107@mail.uic.edu.cn) *  [nicolaslangrene@uic.edu.cn](mailto:nicolaslangrene@uic.edu.cn) [Shengxin.Zhu@bnu.edu.cn](mailto:Shengxin.Zhu@bnu.edu.cn) [  [ 

###### Abstract

This comprehensive review explores the transformative potential of prompt engineering within the realm of large language models (LLMs) and multimodal language models (MMLMs). The development of AI, from its inception in the 1950s to the emergence of neural networks and deep learning architectures, has culminated in sophisticated LLMs such as GPT-4 and BERT, as well as MMLMs like DALL-E and CLIP. These models have revolutionized tasks in diverse fields such as workplace automation, healthcare, and education. Prompt engineering emerges as a crucial technique to maximize the utility and accuracy of these models. This paper delves into both foundational and advanced methodologies of prompt engineering, including techniques like Chain of Thought, Self-consistency, and Generated Knowledge, which significantly enhance model performance. Additionally, it examines the integration of multimodal data through innovative approaches such as Multi-modal Prompt Learning (MaPLe), Conditional Prompt Learning, and Context Optimization. Critical to this discussion is the aspect of AI security, particularly adversarial attacks that exploit vulnerabilities in prompt engineering. Strategies to mitigate these risks and enhance model robustness are thoroughly reviewed. The evaluation of prompt methods is addressed through both subjective and objective metrics, ensuring a robust analysis of their efficacy. This review underscores the pivotal role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.

###### keywords: 

Prompt engineering, LLM, Multimodal, AIGC, Adversarial attacks, AI agent, GPT-4 

##  1 Introduction

The development of Artificial Intelligence (AI) has been marked by significant milestones that have progressively shaped the interaction between humans and machines. The early days of AI research in the 1950s focused on foundational theories and simple problem-solving processes [[1](https://arxiv.org/html/2310.14735v3#bib.bib1)]. As computational power and algorithmic sophistication grew, the 1980s and 1990s saw the emergence of neural networks and the resurgence of machine learning, laying the groundwork for more complex systems [[2](https://arxiv.org/html/2310.14735v3#bib.bib2)].

The introduction of deep learning architectures in the early 2000s, particularly with the development of AlexNet in 2012, marked a pivotal shift towards models capable of handling and interpreting vast amounts of data [[3](https://arxiv.org/html/2310.14735v3#bib.bib3)]. This period set the stage for the evolution of language models, culminating in the creation of transformative models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), which fundamentally changed how machines understand and generate human language [[4](https://arxiv.org/html/2310.14735v3#bib.bib4)].

In recent years, a significant milestone in artificial intelligence research has been the progression of natural language processing capabilities, primarily attributed to large language models (LLMs). Many popular models, rooted in the transformer architecture [[5](https://arxiv.org/html/2310.14735v3#bib.bib5)], undergo training on extensive datasets derived from web-based text. Central to their design is a self-supervised learning objective, which focuses on predicting subsequent words in incomplete sentences. Those models are called Artificial Intelligence-Generated Content (AIGC), and their ability to generate coherent and contextually relevant responses is a result of this training process, where they learn to associate words and phrases with their typical contexts.

LLMs operate by encoding the input text into a high-dimensional vector space, where semantic relationships between words and phrases are preserved. The model then decodes this representation to generate a response, guided by the learned statistical patterns [[6](https://arxiv.org/html/2310.14735v3#bib.bib6)]. The quality of the response can be influenced by various factors, including the prompt provided to the model, the model’s hyperparameters, and the diversity of the training data.

These models, text-only LLMs such as GPT-3 [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)], along with many others (e.g., Google’s BARD [[8](https://arxiv.org/html/2310.14735v3#bib.bib8)], Anthropic’s Claude2 [[9](https://arxiv.org/html/2310.14735v3#bib.bib9)], and Meta’s open-source model LLaMA-2 [[10](https://arxiv.org/html/2310.14735v3#bib.bib10)]), have revolutionized tasks ranging from information extraction to the creation of engaging content [[11](https://arxiv.org/html/2310.14735v3#bib.bib11)]. Parallelly, the development of Multimodal Language Models (MMLMs) has introduced the ability to process and generate not just text, but also images, audio, and other forms of data. These models integrate multiple data modalities into a single framework, enabling more comprehensive understanding and interaction capabilities. Examples of such models include the DALL-E series [[12](https://arxiv.org/html/2310.14735v3#bib.bib12), [13](https://arxiv.org/html/2310.14735v3#bib.bib13), [14](https://arxiv.org/html/2310.14735v3#bib.bib14)], which can generate images from textual descriptions, and CLIP, which can understand and relate text and image data in a unified manner [[15](https://arxiv.org/html/2310.14735v3#bib.bib15)]. More powerful models such as GPT-4 [[16](https://arxiv.org/html/2310.14735v3#bib.bib16)], Google’s Gemini-Ultra [[17](https://arxiv.org/html/2310.14735v3#bib.bib17)], Meta’s LLaMA-3 [[18](https://arxiv.org/html/2310.14735v3#bib.bib18), [19](https://arxiv.org/html/2310.14735v3#bib.bib19)], and Anthropic’s Claude3 [[20](https://arxiv.org/html/2310.14735v3#bib.bib20)] excel in multimodal tasks involving text generation and understanding, integrating natural language processing with various forms of data to perform diverse and complex language-based tasks. The types of data that Multimodal Language Models (MMLMs) are capable of processing are illustrated in Figure [1](https://arxiv.org/html/2310.14735v3#S1.F1 "Figure 1 ‣ 1 Introduction ‣ Unleashing the potential of prompt engineering: a comprehensive review"). This diagram demonstrates that MMLMs can integrate and process data from various modalities, including text, vision, and audio information. This capability allows MMLMs to excel in tasks involving multimodal inputs, showcasing their flexibility and effectiveness. While numerous advanced models are currently capable of processing audio, the majority of accessible API interfaces remain focused on text and vision modalities. With the gradual introduction of audio API interfaces, we can expect a broad and profound expansion of research in this modality [[21](https://arxiv.org/html/2310.14735v3#bib.bib21)]. In conclusion, the evolution of LLMs and MMLMs reflects significant strides in AI research, characterized by increasing model complexity, enhanced training methodologies, and broader application potentials. These advancements underscore the critical role of prompt engineering in maximizing the utility and accuracy of these models, ensuring that they can effectively cater to diverse and dynamic user needs. Since MMLMs are a subset of LMs, references to LMs in the following content inherently include both MMLMs and text-only LLMs.

![Refer to caption](x1.png) Figure 1: Data Modalities of MMLMs

Related to AI systems, the application of LLMs in the workplace has the potential to automate routine tasks, such as data analysis [[22](https://arxiv.org/html/2310.14735v3#bib.bib22)] and text generation [[23](https://arxiv.org/html/2310.14735v3#bib.bib23)], thereby freeing up time for employees to focus on more complex and rewarding tasks [[24](https://arxiv.org/html/2310.14735v3#bib.bib24)]. Furthermore, LLMs have the potential to revolutionize the healthcare sector by assisting in the diagnosis and treatment of diseases. Indeed, by analyzing vast amounts of medical literature, these models can provide doctors with insights into rare conditions, suggest potential treatment pathways, and even predict patient outcomes [[25](https://arxiv.org/html/2310.14735v3#bib.bib25)]. In the realm of education, LLMs can serve as advanced tutoring systems, and promote the quality of teaching and learning [[26](https://arxiv.org/html/2310.14735v3#bib.bib26)]. Those AI tools can analyze a student’s response, identify areas of improvement, and provide constructive feedback in a coherent and formal manner.

In real applications, the prompt is the input of the model, and its engineering can result in significant output difference [[27](https://arxiv.org/html/2310.14735v3#bib.bib27)]. Modifying the structure (e.g., altering length, arrangement of instances) and the content (e.g., phrasing, choice of illustrations, directives) can exert a notable influence on the output generated by the model [[28](https://arxiv.org/html/2310.14735v3#bib.bib28)]. Studies show that both the phrasing and the sequence of examples incorporated within a prompt have been observed to exert a substantial influence on the model’s behavior [[28](https://arxiv.org/html/2310.14735v3#bib.bib28), [29](https://arxiv.org/html/2310.14735v3#bib.bib29)].

Prompt engineering, evolving alongside LLMs, has transitioned from a fundamental practice into a well-structured research domain. As illustrated in Figure [2](https://arxiv.org/html/2310.14735v3#S1.F2 "Figure 2 ‣ 1 Introduction ‣ Unleashing the potential of prompt engineering: a comprehensive review"), the historical progression of prompt engineering showcases significant milestones from the early days of structured inputs to the advanced methodologies developed in recent years. This figure provides a visual timeline of the evolution from basic structured inputs in the 1950s to the sophisticated techniques such as chain-of-thought prompting [[30](https://arxiv.org/html/2310.14735v3#bib.bib30)] and self-consistency prompting [[31](https://arxiv.org/html/2310.14735v3#bib.bib31)] that define the field today. In this paper, we primarily concentrate on the techniques emerging from the period of rapid development subsequent to 2017.

![Refer to caption](x2.png) Figure 2: History of the Development in Prompt Engineering

Prompt engineering refers to the systematic design and optimization of input prompts to guide the responses of LLMs, ensuring accuracy, relevance, and coherence in the generated output. This process is crucial in harnessing the full potential of these models, making them more accessible and applicable across diverse domains. Contemporary prompt engineering encompasses a spectrum of techniques, ranging from foundational approaches such as role-prompting [[32](https://arxiv.org/html/2310.14735v3#bib.bib32)] to more sophisticated methods such as “chain of thought” prompting [[30](https://arxiv.org/html/2310.14735v3#bib.bib30)]. The domain remains dynamic, with emergent research continually unveiling novel techniques and applications in prompt engineering. The importance of prompt engineering is accentuated by its ability to guide model responses, thereby amplifying the versatility and relevance of LLMs in various sectors. Importantly, a well-constructed prompt can counteract challenges such as machine hallucinations, as highlighted in studies by [[33](https://arxiv.org/html/2310.14735v3#bib.bib33)] and [[34](https://arxiv.org/html/2310.14735v3#bib.bib34)]. The influence of prompt engineering extends to numerous disciplines. For instance, it has facilitated the creation of robust feature extractors using LLMs, thereby improving their efficacy in tasks such as defect detection and classification [[35](https://arxiv.org/html/2310.14735v3#bib.bib35)].

In this comprehensive review, we delve into the transformative potential of prompt engineering within the realm of large language models (LLMs). The structure of the paper is organized as follows: We begin with Section [2](https://arxiv.org/html/2310.14735v3#S2 "2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"), which explores the foundational methods of prompt engineering, emphasizing the importance of clear and precise instructions, role-prompting, and iterative attempts to optimize outputs. In Section [3](https://arxiv.org/html/2310.14735v3#S3 "3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"), advanced methodologies such as Chain of Thought, Self-consistency, and Generated Knowledge are introduced to guide models in generating high-quality content. We also discuss methodologies specific to multimodal models in Section [4](https://arxiv.org/html/2310.14735v3#S4 "4 Methodologies of Multimodal LM ‣ Unleashing the potential of prompt engineering: a comprehensive review"), including Multi-modal Prompt Learning (MaPLe), Conditional Prompt Learning, and Context Optimization, which enhance the performance of vision-language models. The efficacy of various prompt methods is assessed through both subjective and objective evaluations, ensuring a robust analysis of their effectiveness (Section [5](https://arxiv.org/html/2310.14735v3#S5 "5 Assessing the efficacy of prompt methods ‣ Unleashing the potential of prompt engineering: a comprehensive review")). The applications of prompt engineering extend across diverse fields such as education, content creation, computer programming, and reasoning tasks, highlighting its broad impact (Section [6](https://arxiv.org/html/2310.14735v3#S6 "6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review")). In Section [7](https://arxiv.org/html/2310.14735v3#S7 "7 Exploring and Enhancing LLMs through Insightful Prompt Engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"), we address the security implications of prompt engineering, identifying common vulnerabilities in LLMs and proposing strategies to enhance security through adversarial training and robust prompt design. Finally, in section [8](https://arxiv.org/html/2310.14735v3#S8 "8 Prospective methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"), we explore prospective methodologies, emphasizing the importance of understanding AI model structures and the potential of AI agents in advancing AI-generated content tools. This structured framework provides a comprehensive overview of the pivotal role of prompt engineering in advancing AI capabilities and guiding future research and applications.

##  2 Basics of prompt engineering

By incorporating just a few key elements, one can craft a basic prompt that enables LLMs to produce high-quality answers. In this section, we discuss some essential components of a well-made prompt.

###  2.1 Model introduction: GPT-4

All of the output in the following sections are generated by GPT-4, developed by OpenAI [[16](https://arxiv.org/html/2310.14735v3#bib.bib16)]. Vast amounts of text data have been used to train GPT-4, whose number of parameters has been estimated to be several orders of magnitude larger than the 175 billion parameters that had been used for the earlier GPT-3 [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)]. The architectural foundation of the model rests on transformers [[5](https://arxiv.org/html/2310.14735v3#bib.bib5)], which essentially are attention mechanisms that assign varying weights to input data based on the context. Similar to GPT-3, GPT-4 was also fine-tuned to follow a broad class of written instructions by reinforcement learning from human feedback (RLHF) [[36](https://arxiv.org/html/2310.14735v3#bib.bib36), [37](https://arxiv.org/html/2310.14735v3#bib.bib37)], which is a technique that uses human preferences as a reward signal to fine-tune models.

When GPT-4 receives an input prompt, the input text will be firstly converted into tokens that the model can interpret and process. These tokens are then managed by transformer layers, which capture their relationships and context. Within these layers, attention mechanisms distribute different weights to tokens based on their relevance and context. After attention processing, the model forms its internal renditions of the input data, known as intermediate representations. These representations are then decoded back into human-readable text [[38](https://arxiv.org/html/2310.14735v3#bib.bib38)].

A significant aspect of this process is the randomness function [[39](https://arxiv.org/html/2310.14735v3#bib.bib39)]. This function is influenced by two primary parameters, temperature and top-k𝑘kitalic_k sampling. The first one, temperature [[40](https://arxiv.org/html/2310.14735v3#bib.bib40)] balances the randomness and determinism in the output. A higher temperature value results in more random outputs, while a lower value makes the output more deterministic. The second one, top-k𝑘kitalic_k sampling [[41](https://arxiv.org/html/2310.14735v3#bib.bib41)], limits the model’s choices to the top k𝑘kitalic_k most probable tokens during each step of output generation. The final stage of this process is the output generation, where the model crafts the final text.

###  2.2 Giving instructions

The method of giving instructions, also considered as re-reading [[42](https://arxiv.org/html/2310.14735v3#bib.bib42)], refers to the heuristics of human reading strategy. It has been observed that the output generated by GPT-4, introduced in Section [2.1](https://arxiv.org/html/2310.14735v3#S2.SS1 "2.1 Model introduction: GPT-4 ‣ 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"), tends to be excessively general when provided with fundamental instructions devoid of any supplementary description [[43](https://arxiv.org/html/2310.14735v3#bib.bib43), [44](https://arxiv.org/html/2310.14735v3#bib.bib44)]. An example prompt is shown in Figure [3](https://arxiv.org/html/2310.14735v3#S2.F3 "Figure 3 ‣ 2.2 Giving instructions ‣ 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"). When the model is prompted with basic instruction, it faces a plethora of options, which makes the result quite broad. Hence, a comprehensive description is imperative to elicit more precise and relevant outputs [[45](https://arxiv.org/html/2310.14735v3#bib.bib45)].

I want to understand some cutting-edge aspects of technology.

Here are some of the key areas:   
1\. Artificial intelligence and machine learning: These technologies are becoming increasingly sophisticated, with advancements in areas such as natural language processing (like the technology you’re interacting with right now), computer vision, and reinforcement learning. Techniques such as transfer learning and federated learning are being used to make machine learning models more efficient and less data-hungry.   
2\. Quantum computing: Quantum computers use the principles of quantum mechanics to process information. They hold the potential to solve certain types of problems much more efficiently than classical computers. Companies like Google, IBM, and many startups are investing heavily in this area.   
3\. 5G and beyond: 5G wireless technology began widespread deployment in 2020 and 2021, bringing faster speeds, lower latency, and the ability to connect more devices simultaneously. Research is also being done on 6G, which is expected to integrate even more deeply with AI and potentially bring about more transformative changes.   
4\. Biotechnology: …   


Model inputModel output

Figure 3: Giving instructions without extra description.

###  2.3 Be clear and precise

The second basic prompt method is “to be clear and precise”. This involves formulating prompts that are unambiguous and specific, which can guide the model toward generating the desired output.

Most LLM architectures are derived from an extensive array of textual data. It can be conceptualized as a combination of insights from a myriad of authors. When presented with a broad or undetailed prompt, its output predominantly exhibits a generic nature, which, while being applicable across a range of contexts, may not be optimal for any specific application. In contrast, a detailed and precise prompt enables the model to generate content that is more aligned with the unique requirements of the given scenario, as it reduces the model’s uncertainty and guides it toward the correct response.

For instance, as shown in Figure [4](https://arxiv.org/html/2310.14735v3#S2.F4 "Figure 4 ‣ 2.3 Be clear and precise ‣ 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"), instead of asking a vague requirement such as “I want to understand the cutting edge of technology.”, a more precise prompt would be “I want to understand the cutting edge of technology, specifically related to artificial intelligence and machine learning…”.

I want to understand the cutting edge of technology, specifically related to artificial intelligence and machine learning. Recently, artificial intelligence has become extremely popular, especially the large language models which have amazed me. Please analyze the possible reasons behind the rapid advancement of this technology from at least three perspectives.

Absolutely, the rise of AI and machine learning, especially with regard to large language models like GPT-4, can be seen as a convergence of several key factors. I’ll analyze this from three perspectives: technological advancements, availability of data, and socio-economic demand. 1\. Technological advancements: The growth of AI and machine learning models has been facilitated by several technological innovations. For starters, we’ve seen incredible improvements in computational power, particularly in Graphics Processing Units (GPUs) and now even more specialized AI chips. These advances have made it … 2\. Availability of data: … 3\. Socio-economic demand: …

Model inputModel output

Figure 4: A clearer and more precise prompt.

###  2.4 Role-prompting

Role-prompting is another fundamental method in prompt engineering. It involves giving the model a specific role to play, such as a helpful assistant or a knowledgeable expert [[46](https://arxiv.org/html/2310.14735v3#bib.bib46)]. This method can be particularly effective in guiding the model’s responses and ensuring that they align with the desired output. For instance, if the model is prompted to act as a historian, it is more likely to provide a detailed and contextually accurate response when asked about a historical event [[47](https://arxiv.org/html/2310.14735v3#bib.bib47)]. Another brief example is shown in Figure [5](https://arxiv.org/html/2310.14735v3#S2.F5 "Figure 5 ‣ 2.4 Role-prompting ‣ 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review").

You are an expert in artificial intelligence specializing in large language models…

Here are five basic methods to quickly get started with large language models: 1\. Experiment with web-based interfaces: … 

A role promptModel output

Figure 5: Role prompting example.

###  2.5 Use of triple quotes to separate

In prompt engineering, the use of triple quotes is a technique used to separate different parts of a prompt or to encapsulate multi-line strings. This technique is particularly useful when dealing with complex prompts that include multiple components or when the prompt itself contains quotes, which makes the model understand one’s instructions better [[48](https://arxiv.org/html/2310.14735v3#bib.bib48)].

###  2.6 Try several times

Due to the non-deterministic nature of LLMs, it is often beneficial to try several times when generating responses. This technique, often referred to as “resampling”, involves running the model multiple times with the same prompt and selecting the best output. This approach can help overcome the inherent variability in the model’s responses and increase the chances of obtaining a high-quality output [[40](https://arxiv.org/html/2310.14735v3#bib.bib40)].

###  2.7 One-shot or few-shot prompting

One-shot and few-shot prompting are two important techniques in prompt engineering. One-shot prompting refers to the method where the model is given a single example to learn from, while few-shot [[49](https://arxiv.org/html/2310.14735v3#bib.bib49)] prompting provides the model with multiple examples [[50](https://arxiv.org/html/2310.14735v3#bib.bib50)]. The choice between one-shot and few-shot prompting often depends on the complexity of the task and the capability of the model. For instance, for simple tasks or highly capable models, one-shot prompting might be sufficient. An example is shown in Figure [6](https://arxiv.org/html/2310.14735v3#S2.F6 "Figure 6 ‣ 2.7 One-shot or few-shot prompting ‣ 2 Basics of prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review"). However, for more complex tasks or less capable models, few-shot prompting can provide additional context and guidance, thereby improving the model’s performance.

Standard Prompt

Q: Yes or no: would a pear sink in water?

A: Yes, a pear would typically float in water, not sink. The reason for this is that pears, like many fruits, are composed primarily of water and have a density that is less than that of water. Objects that are less dense than water will float when placed in water. ×\boldsymbol{\times}bold_×

One-shot Prompt

Q: Yes or no: is it common to see frost during some college commencements?   
A: The answer is yes. Q: Yes or no: would a pear sink in water?

A: The answer is No. \pdfrenderStrokeColor=green,TextRenderingMode=2✓

Figure 6: Comparison of standard prompt and one-shot prompt.

Model inputModel outputModel inputModel output

However, as stated in [[51](https://arxiv.org/html/2310.14735v3#bib.bib51)], “examples don’t always help”. [[51](https://arxiv.org/html/2310.14735v3#bib.bib51)] investigated the intricacies of how large generative language models, such as GPT-3, respond to prompts. One of the significant findings from this paper is that zero-shot prompts can, in certain scenarios, outperform few-shot prompts. This suggests that the role of few-shot examples might not be as much about teaching the model a new task (meta-learning) but rather guiding it to recall a task it has already learned. This insight is crucial as it challenges the conventional wisdom that more examples always lead to better performance [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)]. In the context of one-shot or few-shot prompting, it is essential to understand that while examples can guide the model, they do not always enhance its performance. Sometimes, a well-crafted zero-shot prompt can be more effective than providing multiple examples [[52](https://arxiv.org/html/2310.14735v3#bib.bib52)].

###  2.8 LLM settings: temperature and top-p

The settings of LLMs, such as the temperature and top-p𝑝pitalic_p, play a crucial role in the generation of responses. The temperature parameter controls the randomness of the generated output: a lower temperature leads to more deterministic outputs [[53](https://arxiv.org/html/2310.14735v3#bib.bib53), [54](https://arxiv.org/html/2310.14735v3#bib.bib54)]. The top-p𝑝pitalic_p parameter, on the other hand, controls the nucleus sampling [[40](https://arxiv.org/html/2310.14735v3#bib.bib40)], which is a method to add randomness to the model’s output [[55](https://arxiv.org/html/2310.14735v3#bib.bib55)]. Adjusting these parameters can significantly affect the quality and diversity of the model’s responses, making them essential tools in prompt engineering. However, it has been noted that certain models, exemplified by ChatGPT, do not permit the configuration of these hyperparameters, barring instances where the Application Programming Interface (API) is employed.

##  3 Advanced methodologies

The foundational methods from the previous section can help us produce satisfactory outputs. However, experiments indicate that when using LLMs for complex tasks such as analysis or reasoning, the accuracy of the model’s outputs still has room for improvement. In this section, we will further introduce advanced techniques in prompt engineering to guide the model in generating more specific, accurate, and high-quality content.

###  3.1 Chain of thought

The concept of “Chain of Thought” (CoT) prompting [[30](https://arxiv.org/html/2310.14735v3#bib.bib30)] in LLMs is a relatively new development in the field of AI, and it has been shown to significantly improve the accuracy of LLMs on various logical reasoning tasks [[56](https://arxiv.org/html/2310.14735v3#bib.bib56), [57](https://arxiv.org/html/2310.14735v3#bib.bib57), [58](https://arxiv.org/html/2310.14735v3#bib.bib58)]. CoT prompting involves providing intermediate reasoning steps to guide the model’s responses, which can be facilitated through simple prompts such as “Let’s think step by step” or through a series of manual demonstrations, each composed of a question and a reasoning chain that leads to an answer [[59](https://arxiv.org/html/2310.14735v3#bib.bib59), [60](https://arxiv.org/html/2310.14735v3#bib.bib60)]. It also provides a clear structure for the model’s reasoning process, making it easier for users to understand how the model arrived at its conclusions.

[[61](https://arxiv.org/html/2310.14735v3#bib.bib61)] illustrates the application of CoT prompting to medical reasoning, showing that it can effectively elicit valid intermediate reasoning steps from LLMs. [[62](https://arxiv.org/html/2310.14735v3#bib.bib62)] introduces the concept of Self-Education via Chain-of-Thought Reasoning (SECToR), and argues that, in the spirit of reinforcement learning, LLMs can successfully teach themselves new skills by chain-of-thought reasoning. In another study, [[63](https://arxiv.org/html/2310.14735v3#bib.bib63)] used CoT prompting to train verifiers to solve math word problems, demonstrating the technique’s potential in educational applications. [[64](https://arxiv.org/html/2310.14735v3#bib.bib64)] proposed a multimodal version of CoT, called Multimodal-CoT, to handle more complex, multimodal tasks beyond simple text-based tasks, such as visual tasks, further expanding the potential applications of CoT.

####  3.1.1 Zero-shot chain of thought

The concept of “Zero-Shot Chain of Thought” (Zero-shot-CoT) prompting is an advanced iteration of the CoT prompting mechanism, where the “zero-shot” aspect implies that the model is capable of performing some reasoning without having seen any examples of the task during training.

In their research, [[65](https://arxiv.org/html/2310.14735v3#bib.bib65)] discovered that the augmentation of queries with the phrase “Let’s think step by step” facilitated the generation of a sequential reasoning chain by LLMs. This reasoning chain subsequently proved instrumental in deriving more precise answers. This technique is based on the idea that the model, much like a human, can benefit from having more detailed and logical steps to process the prompt and generate a response.

For instance, the standard prompt is illustrated in Figure [7](https://arxiv.org/html/2310.14735v3#S3.F7 "Figure 7 ‣ 3.1.1 Zero-shot chain of thought ‣ 3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"), while the appended phrase, “Let’s think step by step”, is depicted in Figure [8](https://arxiv.org/html/2310.14735v3#S3.F8 "Figure 8 ‣ 3.1.1 Zero-shot chain of thought ‣ 3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"). Observations indicate that the incorporation of “Let’s think step by step” enhances the logical coherence and comprehensiveness of the model’s response.

Imagine an infinitely wide entrance, which is more likely to pass through it, a military tank or a car?

Given an “infinitely wide entrance”, both a military tank and a car can pass through it without any constraints due to width. Therefore, neither is more likely than the other based on width alone. Both can pass through with equal likelihood. However, if other factors such as ground conditions, accessibility, purpose, or authorization come into play, the likelihood might change. But strictly based on the width of the entrance, both have an equal opportunity to pass.

Model inputModel output

Figure 7: Standard prompt.

Imagine an infinitely wide entrance, which is more likely to pass through it, a military tank or a car? Let’s think step by step.

Given an infinitely wide entrance, both a military tank and a car could pass through without any issues related to the width. However, there are still several factors that could influence which is more likely to pass through: 1\. Ground surface: tanks are designed to handle rough terrains that cars may struggle with. If the ground leading to and from the entrance is too rugged or steep for a car, the tank would be more likely to pass through. 2\. Weight restrictions: if the entrance has a load limit due to the materials used in its construction, the car, being lighter, could be more likely to pass through. The average car weighs around 1.5 tons, while a military tank can weigh anywhere from 60 to 70 tons. 3\. Height clearance: if the entrance is infinitely wide but not infinitely tall, the height of the vehicle matters. The average height of a car is around 1.5 meters, while tanks can be around 2.4 meters. If the entrance is shorter than around 2.4 meters, a car would be more likely to pass through. 4\. Regulations and permissions: … In conclusion, without specific information about the conditions at the entrance, we would assume that a car is more likely to pass through, simply because cars are more common, more likely to meet potential height and weight restrictions and face fewer regulatory barriers.

Model inputModel output

Figure 8: Adding “Let’s think step by step”.

####  3.1.2 Golden chain of thought

[[66](https://arxiv.org/html/2310.14735v3#bib.bib66)] introduced the “golden chain of thought”, providing an innovative approach to generating responses to instruction-based queries. This methodology leverages a set of “ground-truth chain-of-thought” solutions incorporated within the prompt, considerably simplifying the task for the model as it circumvents the necessity for independent CoT generation. Concurrently, a novel benchmark comprising detective puzzles has been designed, to assess the abductive reasoning capacities of LLMs, which is also considered an evaluation of the golden CoT. Finally, according to [[66](https://arxiv.org/html/2310.14735v3#bib.bib66)]’s experiment, in the context of the golden CoT, GPT-4 exhibits commendable performance, boasting an 83% solve rate in contrast to the 38% solve rate of the standard CoT.

However, the characteristics of the Golden CoT requiring the “ground-truth chain-of-thought solutions” as an integral part of the prompt also signifies that the Golden CoT’s contribution to solving such problems is limited, despite its high solve rate of 83%.

###  3.2 Self-consistency

In the assessment of INSTRUCTGPT [[67](https://arxiv.org/html/2310.14735v3#bib.bib67)] and GPT-3 [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)] on a new synthetic QA dataset called PRONTOQA, for Proof and Ontology-Generated Question-Answering [[68](https://arxiv.org/html/2310.14735v3#bib.bib68), [69](https://arxiv.org/html/2310.14735v3#bib.bib69)], it was observed that although the most extensive model exhibited capability in reasoning tasks, it encountered challenges in proof planning and the selection of the appropriate proof step amidst multiple options, which caused accuracy uncertainties [[68](https://arxiv.org/html/2310.14735v3#bib.bib68)]. Self-consistency in LLMs is an advanced prompting technique that aims to ensure the model’s responses are consistent with each other [[31](https://arxiv.org/html/2310.14735v3#bib.bib31), [30](https://arxiv.org/html/2310.14735v3#bib.bib30)], which greatly increases the odds of obtaining highly accurate results. The principle of self-consistency in language models posits that for a complex reasoning problem, there can be multiple reasoning paths leading to the correct answer. In this approach, a language model generates a diverse set of reasoning paths for the same problem. The most accurate and consistent answer is then determined by evaluating and marginalizing across these varied paths, ensuring that the final answer reflects the convergence of multiple lines of thought.

The self-consistency method contains three steps. Firstly, prompt a language model using CoT prompting, then replace the “greedy decode” (1-Best) [[39](https://arxiv.org/html/2310.14735v3#bib.bib39), [70](https://arxiv.org/html/2310.14735v3#bib.bib70)] in CoT prompting by sampling from the language model’s decoder to generate a diverse set of reasoning paths, and finally, marginalize out the reasoning paths and aggregate by choosing the most consistent answer in the final answer set.

It is noteworthy that self-consistency can be harmoniously integrated with most sampling algorithms, including but not limited to, temperature sampling [[53](https://arxiv.org/html/2310.14735v3#bib.bib53), [54](https://arxiv.org/html/2310.14735v3#bib.bib54)], top-k𝑘kitalic_k sampling [[71](https://arxiv.org/html/2310.14735v3#bib.bib71), [72](https://arxiv.org/html/2310.14735v3#bib.bib72), [39](https://arxiv.org/html/2310.14735v3#bib.bib39)], and nucleus sampling [[40](https://arxiv.org/html/2310.14735v3#bib.bib40)]. Nevertheless, such an operation may necessitate the invocation of the model’s Application Programming Interface (API) to fine-tune these hyperparameters. In light of this, an alternative approach could be to allow the model to generate results employing diverse reasoning paths, and then generate a diverse set of candidate reasoning paths. The response demonstrating the highest degree of consistency across the various reasoning trajectories is then more inclined to represent the accurate solution [[73](https://arxiv.org/html/2310.14735v3#bib.bib73)].

Studies have shown that self-consistency enhances outcomes in arithmetic, commonsense, and symbolic reasoning tasks [[74](https://arxiv.org/html/2310.14735v3#bib.bib74), [6](https://arxiv.org/html/2310.14735v3#bib.bib6)]. Furthermore, in practice, self-consistency can be combined with other techniques to further enhance the model’s performance. For example, a study found that combining self-consistency with a discriminator-guided multi-step reasoning approach significantly improved the model’s reasoning capabilities [[75](https://arxiv.org/html/2310.14735v3#bib.bib75)].

###  3.3 Generated knowledge

The “generated knowledge” [[76](https://arxiv.org/html/2310.14735v3#bib.bib76)] approach in prompt engineering is a technique that leverages the ability of LLMs to generate potentially useful information about a given question or prompt before generating a final response. This method is particularly effective in tasks that require commonsense reasoning, as it allows the model to generate and utilize additional context that may not be explicitly present in the initial prompt.

As exemplified in Figure [7](https://arxiv.org/html/2310.14735v3#S3.F7 "Figure 7 ‣ 3.1.1 Zero-shot chain of thought ‣ 3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"), when posing the query to the model, “Imagine an infinitely wide entrance, which is more likely to pass through it, a military tank or a car?”, standard prompts predominantly yield responses that neglect to factor in the “entrance height”. Conversely, as delineated in Figure [9](https://arxiv.org/html/2310.14735v3#S3.F9 "Figure 9 ‣ 3.3 Generated knowledge ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review") and Figure [10](https://arxiv.org/html/2310.14735v3#S3.F10 "Figure 10 ‣ 3.3 Generated knowledge ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review"), prompting the model to first generate pertinent information and subsequently utilizing generated information in the query leads to outputs with augmented logical coherence and comprehensiveness. Notably, this approach stimulates the model to account for salient factors such as “entrance height”.

Generate two key analyses related to detailed size data on military tanks and cars, and then generate three key influencing factors regarding whether an object can pass through an infinitely wide entrance.

Detailed size analysis: military tanks vs. cars 1\. Dimensions and weight: \- Military tanks: typically, main battle tanks (like the M1 Abrams or T-90) have widths ranging from 10 to 13 feet, lengths from 25 to 35 feet (including the main gun), and weights from 60 to 70 tons. \- Cars: most consumer cars have a width of about 6 to 8 feet, a length of 12 to 20 feet, and weigh between 1 to 3 tons. Some large SUVs and trucks can exceed these figures but are still substantially smaller and lighter than tanks. 2\. Maneuverability & design: \- Military tanks: tanks are designed to traverse rough terrains and have tracks that distribute weight and prevent the vehicle from getting easily stuck in muddy or uneven ground. However, their sheer weight and size limit their maneuverability in urban or congested settings. \- Cars: designed for paved roads, cars are generally more agile and can navigate tight spaces more efficiently than tanks. Their design emphasizes aerodynamics and comfort, unlike tanks that prioritize armor and firepower.   
Three key influencing factors on whether an object can pass through an infinitely wide entrance: 1\. Height of the object: the height is critical. An infinitely wide entrance does not give information about its height. If the object is taller than the entrance, it won’t pass through. 2\. Structural integrity: depending on the material and design of an object, squeezing or forcing it through an entrance could compromise its structural integrity. For instance, certain tanks have reactive armor or sensitive equipment that may be damaged if scraped or bumped forcefully. 3\. Ground clearance and maneuverability: …

Model inputModel output

Figure 9: Generating knowledge (Step1).

“““ Detailed size analysis: military tanks vs. cars 1\. Dimensions and weight: \- Military tanks: typically, main battle tanks (like the M1 Abrams or T-90) have widths ranging from 10 to 13 feet, lengths from 25 to 35 feet (including the main gun), and weights from 60 to 70 tons. \- Cars: most consumer cars have a width of about 6 to 8 feet, a length of 12 to 20 feet, and weigh between 1 to 3 tons. Some large SUVs and trucks can exceed these figures but are still substantially smaller and lighter than tanks. 2\. Maneuverability & design… ”””   
Use the above information to answer the following question: Imagine an infinitely wide entrance, which is more likely to pass through it, a military tank or a car? 

Given the information provided about the dimensions, design, and the factors influencing whether an object can pass through an infinitely wide entrance, we can infer the following: 1\. Height of the object: the height is not specified for either tanks or cars. However, given that tanks are built for rough terrains and often have a turret and other equipment on top, they generally have a taller profile compared to most cars. If the entrance had a restrictive height, cars would generally have an advantage here. 2\. Structural integrity: … 3\. Ground clearance and maneuverability:   
… Conclusion: If the entrance height is not restrictive and the surface is rough, a military tank would be more likely to pass through it without issues. On the other hand, if the entrance is low-height or has tight squeezes that could compromise the structural integrity of larger, robust vehicles, a car might be at an advantage. However, given no height constraints and only the factor of width (since it is “infinitely wide”), both a military tank and a car would easily pass through it. In such a scenario, the distinguishing factor would be the terrain of the entrance. If it is rough or uneven, the tank has the advantage. If it is smooth, both would pass with relative ease.

Model inputModel output

Figure 10: Combining the generated knowledge with the question (Step2).

###  3.4 Least-to-most prompting

The concept of “least to most prompting” [[77](https://arxiv.org/html/2310.14735v3#bib.bib77)] is an advanced method that involves starting with a minimal prompt and gradually increasing its complexity to elicit more sophisticated responses from the language model. The foundational premise of this approach is the decomposition of intricate problems into a succession of more rudimentary subproblems, which are then sequentially addressed. The resolution of each subproblem is expedited by leveraging solutions derived from antecedent subproblems.

Upon rigorous experimentation in domains including symbolic manipulation, compositional generalization, and mathematical reasoning, findings from [[77](https://arxiv.org/html/2310.14735v3#bib.bib77)] substantiate that the least-to-most prompting paradigm exhibits the capacity to generalize across challenges of greater complexity than those initially presented in the prompts. They found that LLMs seem to respond effectively to this method, demonstrating its potential for enhancing the reasoning capabilities of these models.

###  3.5 Tree of thoughts

The “tree of thoughts” (ToT) prompting technique in LLMs is an advanced method that employs a structured approach to guide LLMs in their reasoning and response generation processes. It enhances problem-solving by exploring multiple reasoning paths, termed ’thoughts’. Unlike traditional linear prompts, ToT allows LLMs to consider various possible solutions and strategies, including looking ahead, backtracking, and self-evaluation, making it more interactive and adaptable to the complexity of the task at hand. This approach fosters more dynamic and deliberate decision-making in complex problem-solving tasks, moving beyond a rigid, hierarchical prompt structure to a more flexible and adaptive reasoning process [[78](https://arxiv.org/html/2310.14735v3#bib.bib78)]. The ToT approach, for instance, when applied to complex mathematical problem-solving, prompts the model to generate various potential solutions and evaluate them, rather than simply asking for a solution.

[[78](https://arxiv.org/html/2310.14735v3#bib.bib78)] demonstrates that this formulation is more versatile and can handle challenging tasks where standard prompts might fall short. Another research by [[79](https://arxiv.org/html/2310.14735v3#bib.bib79)] further emphasizes the potential of this technique in enhancing the performance of LLMs by structuring their thought processes.

[[8](https://arxiv.org/html/2310.14735v3#bib.bib8)] introduces the “tree-of-thought prompting”, an approach that assimilates the foundational principles of the ToT frameworks and transforms them into a streamlined prompting methodology. This technique enables LLMs to assess intermediate cognitive constructs within a singular prompt. An exemplar ToT prompt is delineated in Figure [11](https://arxiv.org/html/2310.14735v3#S3.F11 "Figure 11 ‣ 3.5 Tree of thoughts ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review") [[8](https://arxiv.org/html/2310.14735v3#bib.bib8)].

Imagine three different experts answering this question.   
All experts will write down 1 step of their thinking,   
then share it with the group.   
Then all experts will go on to the next step, etc.   
If any expert realizes they’re wrong at any point then they leave.   
The question is…

Tree of thoughts prompting

Figure 11: A sample ToT prompt.

###  3.6 Graph of thoughts

Unlike the “chain of thoughts” or “tree of thoughts” paradigms, the “graph of thoughts” (GoT) framework [[80](https://arxiv.org/html/2310.14735v3#bib.bib80)] offers a more intricate method of representing the information generated by LLMs. The core concept behind GoT is to model this information as an arbitrary graph. In this graph, individual units of information, termed “LLM thoughts”, are represented as vertices. The edges of the graph, on the other hand, depict the dependencies between these vertices. This unique representation allows for the combination of arbitrary LLM thoughts, thereby creating a synergistic effect in the model’s outputs.

In the context of addressing intricate challenges, LLMs utilizing the GoT framework might initially produce several autonomous thoughts or solutions. These individual insights can subsequently be interlinked based on their pertinence and interdependencies, culminating in a detailed graph. This constructed graph permits diverse traversal methods, ensuring the final solution is both precise and comprehensive, encompassing various dimensions of the challenge.

The efficacy of the GoT framework is anchored in its adaptability and the profound insights it can yield, particularly for intricate issues necessitating multifaceted resolutions. Nonetheless, it is imperative to recognize that while GoT facilitates a systematic approach to problem-solving, it also necessitates a profound comprehension of the subject matter and meticulous prompt design to realize optimal outcomes [[81](https://arxiv.org/html/2310.14735v3#bib.bib81)].

###  3.7 Automatic Reasoning and Tool Usage

Automatic Reasoning and Tool Usage (ART) is an advanced prompting technique that combines the principles of automatic chain-of-thought (CoT) prompting with the strategic utilization of external tools. This method aims to enhance the reasoning capabilities of Large Language Models (LLMs) by guiding them through multi-step reasoning processes and leveraging specialized tools to achieve more accurate and relevant outputs.

ART builds on the CoT prompting technique, which encourages models to generate intermediate reasoning steps before arriving at a final answer. In ART, these reasoning steps are augmented by incorporating external tools such as calculators, databases, or other software applications. The integration of tools helps LLMs perform tasks that require precise calculations, access to updated information, or specialized data processing that the model alone may not handle effectively.

For example, a prompt designed using ART might guide an LLM to first outline the steps required to solve a complex mathematical problem and then use a calculator tool to perform the necessary calculations. This combination of reasoning and tool usage ensures that the model’s outputs are both logically coherent and computationally accurate.

Recent advancements in prompt engineering have highlighted the potential of ART in improving LLM performance. Studies have demonstrated that ART can help models navigate complex problem spaces more effectively by breaking down tasks into manageable steps and utilizing appropriate tools at each stage [[82](https://arxiv.org/html/2310.14735v3#bib.bib82)]. For instance, the integration of ART in natural language processing tasks has shown promising results in areas such as automated customer service, where models need to access and process information dynamically [[83](https://arxiv.org/html/2310.14735v3#bib.bib83)].

Moreover, ART’s approach aligns with ongoing efforts to develop more robust and versatile AI systems capable of handling real-world tasks that demand a combination of cognitive and computational skills. Research by Mercity AI (2024) explores advanced techniques in ART to achieve better accuracy and reliability in AI applications [[84](https://arxiv.org/html/2310.14735v3#bib.bib84)]. These findings underscore the importance of ART in enhancing the functionality and performance of LLMs, making them more adept at handling a broader range of tasks.

The applications and benefits of ART are substantial. By leveraging external tools, ART can significantly improve the accuracy of the model’s responses, especially for tasks that require specific and precise outputs, such as financial calculations or data analysis. Additionally, ART expands the range of tasks that LLMs can effectively handle by integrating specialized tools. This makes it possible for LLMs to perform more complex and varied tasks, from technical problem-solving to real-time data retrieval. For end-users, ART can facilitate more intuitive and efficient interactions with LLMs. Users can rely on the model to handle multifaceted queries that involve multiple steps and require the use of different tools, enhancing the overall user experience.

In conclusion, ART represents a significant advancement in prompt engineering, combining logical reasoning with practical tool usage to improve the performance and reliability of LLMs. As research continues to evolve, the integration of ART techniques is likely to play a crucial role in developing more sophisticated and capable AI systems.

###  3.8 Active-Prompt

Active-Prompt is an innovative prompt engineering technique that involves dynamically modulating prompts based on responsive feedback from the model or user interactions. This approach is designed to enhance the engagement between the user and the Large Language Model (LLM), ensuring that the prompts are continuously refined to elicit more accurate and contextually relevant responses.

Active-Prompt operates on the principle of interactive feedback loops. Unlike static prompts, which are predetermined and unchanging, active prompts adapt based on the responses received from the LLM. This dynamic adjustment allows for a more iterative and responsive interaction, where the model’s preliminary outputs are used to fine-tune subsequent prompts. This process can involve human annotators or automated systems that evaluate the responses and provide feedback to improve prompt effectiveness [[85](https://arxiv.org/html/2310.14735v3#bib.bib85)].

For instance, in a customer service application, an active prompt might initially query the model about a general issue. Based on the model’s response, the prompt could then be adjusted to ask more specific questions, guiding the conversation towards a precise resolution. This method ensures that the interaction remains relevant and progressively narrows down the possible solutions, enhancing the overall accuracy and satisfaction of the response.

By continuously refining prompts based on feedback, Active-Prompt can significantly improve the accuracy and relevance of the model’s responses. This is particularly beneficial in complex or nuanced conversations where initial responses may require further clarification [[86](https://arxiv.org/html/2310.14735v3#bib.bib86)]. Active-Prompt allows for a high degree of personalization in interactions. By adapting prompts to user responses, it can create a more engaging and tailored experience, which is crucial in applications such as customer support, personalized learning, and interactive AI companions [[87](https://arxiv.org/html/2310.14735v3#bib.bib87)]. For end-users, Active-Prompt facilitates more intuitive and efficient interactions with LLMs. Users benefit from a model that appears more responsive and capable of understanding context, leading to more productive and satisfying interactions [[88](https://arxiv.org/html/2310.14735v3#bib.bib88)].

Recent studies and practical applications have demonstrated the efficacy of Active-Prompt in various domains. Research by Spiceworks (2024) highlights how Active-Prompt can dynamically adjust to user feedback, improving the overall quality of model interactions in real-time. This approach aligns with the broader trend towards more interactive and adaptive AI systems, emphasizing the importance of responsive design in prompt engineering. Moreover, the concept of Active-Prompt aligns with ongoing efforts to develop more human-like conversational agents. By integrating feedback loops and interactive adjustments, Active-Prompt contributes to the creation of AI systems that are not only more accurate but also more engaging and user-friendly.

In conclusion, Active-Prompt represents a significant advancement in prompt engineering, offering a dynamic and responsive approach to interacting with LLMs. As research in this area continues to evolve, the integration of Active-Prompt techniques is likely to play a crucial role in developing more sophisticated and effective AI systems.

###  3.9 ReAct Framework

The ReAct Framework, short for Reasoning and Acting, represents a cutting-edge approach in prompt engineering aimed at enhancing the interaction capabilities of Large Language Models (LLMs). This method synergizes the processes of reasoning and action to enable LLMs to not only think through problems but also interact with external tools and environments to achieve more accurate and contextually appropriate outcomes.

The ReAct Framework operates by prompting LLMs to generate both reasoning traces and task-specific actions. This dual approach ensures that the model first contemplates the problem, devising a logical sequence of thoughts, and then executes actions that may involve querying external databases, using calculators, or interacting with other software tools. This method is particularly effective in scenarios requiring detailed reasoning followed by specific actions, thus ensuring the LLM can handle complex, multi-step tasks efficiently [[89](https://arxiv.org/html/2310.14735v3#bib.bib89)].

For example, in a task involving financial analysis, the ReAct framework would first prompt the LLM to outline the necessary steps to evaluate a portfolio. Subsequently, the model could use financial analysis tools to gather current market data and perform calculations, integrating these results into the final analysis. This combination of reasoning and action leads to more robust and reliable outcomes compared to using static prompts alone.

The ReAct Framework offers several notable advantages. By integrating reasoning and action, ReAct enables LLMs to make more informed and accurate decisions. This is particularly valuable in fields such as finance, healthcare, and legal analysis, where decisions must be based on comprehensive data and logical reasoning [[90](https://arxiv.org/html/2310.14735v3#bib.bib90)]. Additionally, the framework’s ability to interact with external tools ensures that the information used in decision-making is up-to-date and relevant, reducing the likelihood of errors due to outdated or incomplete data [[91](https://arxiv.org/html/2310.14735v3#bib.bib91)]. Furthermore, ReAct expands the range of tasks LLMs can perform by enabling them to act on their reasoning. This makes the framework suitable for a variety of applications, from customer service automation to scientific research, where complex, multi-step processes are common [[92](https://arxiv.org/html/2310.14735v3#bib.bib92)].

Research on the ReAct Framework underscores its potential in transforming how LLMs handle complex tasks. Studies by Yao et al. (2022) highlight how the integration of reasoning and action in prompts can significantly improve task performance in LLMs. Additionally, practical applications have demonstrated the framework’s effectiveness in real-world scenarios, such as dynamic data retrieval and interactive problem-solving [[89](https://arxiv.org/html/2310.14735v3#bib.bib89), [90](https://arxiv.org/html/2310.14735v3#bib.bib90)].

Implementing the ReAct Framework involves developing prompts that guide LLMs through both thought processes and actions. This requires a detailed understanding of the task at hand and the tools available, ensuring that the model can seamlessly transition from reasoning to action. As the field of prompt engineering evolves, the ReAct Framework is likely to become an essential tool for developing more versatile and capable AI systems.

In conclusion, the ReAct Framework represents a significant advancement in prompt engineering, combining logical reasoning with actionable steps to enhance the performance and reliability of LLMs. As research continues to expand on this approach, the framework’s integration into various AI applications promises to drive further innovation and effectiveness in AI-driven tasks.

###  3.10 Retrieval augmentation

Another direction of prompt engineering is to aim to reduce hallucinations. When using AIGC tools such as GPT-4, it is common to face a problem called “hallucinations”, which refer to the presence of unreal or inaccurate information in the model’s generated output [[93](https://arxiv.org/html/2310.14735v3#bib.bib93), [33](https://arxiv.org/html/2310.14735v3#bib.bib33)]. While these outputs may be grammatically correct, they can be inconsistent with facts or lack real-world data support. Hallucinations arise because the model may not have found sufficient evidence in its training data to support its responses, or it may overly generalize certain patterns when attempting to generate fluent and coherent output [[94](https://arxiv.org/html/2310.14735v3#bib.bib94)].

An approach to reduce hallucinations and enhance the effectiveness of prompts is the so-called retrieval augmentation technique, which aims at incorporating up-to-date external knowledge into the model’s input [[95](https://arxiv.org/html/2310.14735v3#bib.bib95), [96](https://arxiv.org/html/2310.14735v3#bib.bib96)]. It is emerging as an AI framework for retrieving facts from external sources. [[97](https://arxiv.org/html/2310.14735v3#bib.bib97)] examines the augmentation of context retrieval through the incorporation of external information. It proposes a sophisticated operation: the direct concatenation of pertinent information obtained from an external source to the prompt, which is subsequently treated as foundational knowledge for input into the expansive language model. Additionally, the paper introduces auto-regressive techniques for both retrieval and decoding, facilitating a more nuanced approach to information retrieval and fusion. This research demonstrates that in-context retrieval-augmented language models [[97](https://arxiv.org/html/2310.14735v3#bib.bib97)], when constructed upon readily available general-purpose retrievers, yield significant LLM enhancements across a variety of model dimensions and diverse corpora. In another research, [[98](https://arxiv.org/html/2310.14735v3#bib.bib98)] showed that GPT-3 can reduce hallucinations by studying various implementations of the retrieval augmentation concept, such as Retrieval Augmented Generation (RAG) [[99](https://arxiv.org/html/2310.14735v3#bib.bib99)], Fusion-in-Decoder (FiD) [[100](https://arxiv.org/html/2310.14735v3#bib.bib100)], Seq2seq [[101](https://arxiv.org/html/2310.14735v3#bib.bib101), [102](https://arxiv.org/html/2310.14735v3#bib.bib102), [103](https://arxiv.org/html/2310.14735v3#bib.bib103)] and others. [[104](https://arxiv.org/html/2310.14735v3#bib.bib104)] developed the Chain-of-Verification (CoVe) approach to reduce hallucinations, based on letting the LLM deliberate on its own responses before self-correcting them. They suspect that extending this approach with retrieval augmentation would likely bring further gains.

###  3.11 Use plugins to polish the prompts

After introducing the detailed techniques and methods of prompt engineering, we now explore the use of some external prompt engineering assistants that have been developed recently and exhibit promising potential. Unlike the methods introduced previously, these instruments can help us to polish the prompt directly. They are adept at analyzing user inputs and subsequently producing pertinent outputs within a context that is defined by itself, thereby amplifying the efficacy of prompts. Some of the plugins provided by OpenAI are good examples of such tools [[105](https://arxiv.org/html/2310.14735v3#bib.bib105)].

In certain implementations, the definition of a plugin is incorporated into the prompt, potentially altering the output [[106](https://arxiv.org/html/2310.14735v3#bib.bib106)]. Such integration may impact the manner in which LLMs interpret and react to the prompts, illustrating a connection between prompt engineering and plugins. Furthermore, the laborious nature of intricate prompt engineering may be mitigated by plugins, which enable the model to more proficiently comprehend or address user inquiries without necessitating excessively detailed prompts. Consequently, plugins bolster the efficacy of prompt engineering while promoting enhanced user-centric efficiency. These tools, akin to packages, can be seamlessly integrated into Python and invoked directly [[107](https://arxiv.org/html/2310.14735v3#bib.bib107), [108](https://arxiv.org/html/2310.14735v3#bib.bib108)]. Such plugins augment the efficacy of prompts by furnishing responses that are both coherent and contextually pertinent. For instance, the “Prompt Enhancer” pluging [[109](https://arxiv.org/html/2310.14735v3#bib.bib109)], developed by AISEO [[110](https://arxiv.org/html/2310.14735v3#bib.bib110)], can be invoked by starting the prompt with the word “AISEO” to let the AISEO prompt generator automatically enhance the LLM prompt provided. Similarly, another plugin called “Prompt Perfect”, can be used by starting the prompt with ‘perfect’ to automatically enhance the prompt, aiming for the “perfect” prompt for the task at hand [[111](https://arxiv.org/html/2310.14735v3#bib.bib111), [112](https://arxiv.org/html/2310.14735v3#bib.bib112)].

##  4 Methodologies of Multimodal LM

Multimodal language models (MMLMs) represent a significant advancement in artificial intelligence, enabling the processing and generation of information across multiple modalities, such as text, images, and audio. This capability enhances the versatility and applicability of AI systems in diverse fields, from content creation to complex reasoning tasks. Prompt engineering in MMLMs involves adapting traditional techniques used in text-only LLMs to handle the intricacies of multimodal data. These models must seamlessly integrate and interpret various data types, requiring sophisticated prompt designs that ensure contextual coherence and accuracy [[113](https://arxiv.org/html/2310.14735v3#bib.bib113)].

Challenges such as data alignment, modality integration, and context preservation are addressed through modular prompting and disentangled prompt design [[114](https://arxiv.org/html/2310.14735v3#bib.bib114)]. These advancements facilitate the effective utilization of MMLMs in various applications, enhancing their ability to generate nuanced and contextually rich outputs [[115](https://arxiv.org/html/2310.14735v3#bib.bib115)]. Pioneering studies have demonstrated the potential of these methods, highlighting the transformative impact of prompt engineering on multimodal AI systems [[116](https://arxiv.org/html/2310.14735v3#bib.bib116)].

Prompt engineering for multimodal large language models (MLLMs) builds on the foundational techniques used in text-only LLMs but adapts them to accommodate the complexity of multimodal data. Traditional prompt methods, such as few-shot and zero-shot learning, are modified to handle diverse data types, including text, images, and audio. This adaptation is crucial as MLLMs must integrate and process multiple modalities simultaneously, requiring innovative approaches to ensure coherence and relevance in generated responses [[117](https://arxiv.org/html/2310.14735v3#bib.bib117)]. These advancements facilitate the effective utilization of MLLMs in various applications, enhancing their ability to generate nuanced and contextually rich outputs [[118](https://arxiv.org/html/2310.14735v3#bib.bib118)]. Pioneering studies have demonstrated the potential of these methods, highlighting the transformative impact of prompt engineering on multimodal AI systems [[119](https://arxiv.org/html/2310.14735v3#bib.bib119), [113](https://arxiv.org/html/2310.14735v3#bib.bib113)].

###  4.1 Multi-modal Prompt Learning (MaPLe)

Multi-modal Prompt Learning (MaPLe) represents a significant advancement in the fine-tuning of vision-language models, leveraging the integration of both vision and language prompts to enhance model performance. This methodology is predicated on the hierarchical learning of prompts within the transformer layers of the model, allowing for a more nuanced and comprehensive capture of task-relevant features. The core idea behind MaPLe is to introduce and optimize prompts for both the vision and language components simultaneously. By embedding prompts at various stages within the transformer architecture, MaPLe ensures that the model can adaptively learn contextual information pertinent to the specific task at hand [[120](https://arxiv.org/html/2310.14735v3#bib.bib120)]). This hierarchical approach allows the model to progressively refine its understanding and integration of multimodal inputs, leading to improved performance across a range of applications.

One of the critical innovations of MaPLe is its ability to enhance task relevance. Traditional prompt engineering often focuses on either vision or language prompts in isolation, which can limit the model’s ability to fully leverage the complementary information available in multimodal data. MaPLe overcomes this limitation by jointly optimizing prompts for both modalities, thereby facilitating a more integrated and coherent representation of the input data [[120](https://arxiv.org/html/2310.14735v3#bib.bib120), [121](https://arxiv.org/html/2310.14735v3#bib.bib121)].

The effectiveness of MaPLe has been demonstrated in various studies. For example, [[120](https://arxiv.org/html/2310.14735v3#bib.bib120)] showed that MaPLe significantly outperforms baseline models in tasks such as image recognition and visual question answering. Furthermore, [[121](https://arxiv.org/html/2310.14735v3#bib.bib121)] highlighted the importance of multi-modal prompt learning in enhancing the adaptability and generalization of vision-language models.

Another important aspect of MaPLe is its hierarchical learning mechanism, which allows the model to process and integrate information at multiple levels of abstraction. This is particularly beneficial for complex tasks that require a deep understanding of both visual and textual elements. By optimizing prompts at different layers within the transformer, MaPLe can better capture the intricate dependencies between vision and language inputs [[120](https://arxiv.org/html/2310.14735v3#bib.bib120), [122](https://arxiv.org/html/2310.14735v3#bib.bib122)].

To illustrate the practical application of MaPLe, consider the task of Visual Question Answering (VQA). In a typical VQA scenario, a model is provided with an image and a related question, and it must generate a correct and contextually relevant answer. Using MaPLe, the model can be fine-tuned with multi-modal prompts that simultaneously address both the visual content and the textual question. For instance, given an image of a bustling market and the question ”What fruit is the vendor selling?”, MaPLe would embed prompts at various levels of the transformer’s vision and language branches. These prompts might include visual prompts that focus on identifying objects and text prompts that guide the model to look for specific answer-relevant details. By processing these prompts hierarchically, the model can effectively integrate visual cues (like recognizing apples and oranges in the image) with the textual context (understanding the question) to generate an accurate answer (e.g., ”The vendor is selling apples and oranges”).

This multi-modal approach ensures that the model leverages both the visual and textual information in a coherent and integrated manner, resulting in improved performance on VQA tasks compared to models that do not utilize such comprehensive prompt learning strategies.

In conclusion, MaPLe represents a robust and effective methodology for improving the performance of vision-language models. Its ability to integrate and optimize multi-modal prompts within the transformer architecture ensures that the model can adaptively learn and generalize across a wide range of tasks, making it a valuable tool in the field of multimodal AI.

###  4.2 Context Optimization

Context Optimization (CoOp) is an innovative approach to prompt learning specifically designed for vision-language models. CoOp focuses on enhancing the adaptability and performance of these models by optimizing context-specific prompts. This methodology involves the creation of learnable context vectors that are embedded within the model’s architecture, enabling it to dynamically adjust to different downstream tasks [[123](https://arxiv.org/html/2310.14735v3#bib.bib123)].

CoOp leverages the inherent structure of vision-language models, such as CLIP, by integrating context optimization directly into the model’s training process. This integration allows for a seamless adaptation to various tasks without the need for extensive manual prompt engineering. By utilizing learnable context vectors, CoOp can fine-tune the prompts based on the specific characteristics of the input data, leading to improved performance and generalization across different scenarios [[124](https://arxiv.org/html/2310.14735v3#bib.bib124)].

One of the key benefits of CoOp is its ability to handle diverse and complex data inputs. Traditional static prompts can be limited in their ability to adapt to new and unseen data, whereas CoOp’s dynamic approach allows the model to learn and adjust in real-time. This is particularly valuable in applications such as image recognition and visual question answering, where the context can vary significantly [[125](https://arxiv.org/html/2310.14735v3#bib.bib125)].

To illustrate the practical application of CoOp, consider a Visual Question Answering (VQA) task. In a VQA scenario, the model is presented with an image and a corresponding question, and it must generate an accurate answer based on the visual and textual information. Using CoOp, the model can be trained with context-specific prompts that enhance its ability to interpret the image and understand the question.

For instance, if the model is shown an image of a beach scene with the question ”What activity are the people engaged in?”, CoOp would utilize learnable context vectors to focus on relevant aspects of the image, such as identifying people, recognizing activities, and understanding the scene’s context. This enables the model to generate a precise and contextually relevant answer, such as ”The people are playing volleyball on the beach” [[123](https://arxiv.org/html/2310.14735v3#bib.bib123)].

The effectiveness of CoOp has been demonstrated in various studies. Zhou et al. (2021) [1] showed that models using CoOp significantly outperform traditional models in tasks such as image recognition and VQA. Additionally, [[125](https://arxiv.org/html/2310.14735v3#bib.bib125)] highlighted the benefits of ensembling context optimization, which further enhances the model’s performance by combining multiple context vectors. This approach has been shown to improve the robustness and generalization of vision-language models in real-world applications (Unified Adapter and Prompt Learning, 2023) [[126](https://arxiv.org/html/2310.14735v3#bib.bib126)].

In summary, Context Optimization (CoOp) represents a powerful methodology for enhancing the performance and adaptability of vision-language models. By leveraging learnable context vectors, CoOp enables these models to dynamically adjust to different tasks and data inputs, making it a valuable tool in the field of multimodal AI.

###  4.3 Conditional Prompt Learning

Conditional Prompt Learning is a methodology that enhances the adaptability and performance of vision-language models by tailoring prompts based on specific conditions or contexts. This technique allows models to dynamically adjust their responses to better fit the requirements of various tasks, thereby improving their overall effectiveness and versatility.

The core concept of Conditional Prompt Learning involves the creation of prompts that are conditionally tailored to the specific input or task context. This method stands in contrast to static prompts that remain the same regardless of the situation. By leveraging contextual information, conditional prompts can provide more precise and relevant guidance to the model, which is particularly useful in complex, multimodal scenarios where the interplay between different types of data must be carefully managed [[127](https://arxiv.org/html/2310.14735v3#bib.bib127)].

One significant advantage of Conditional Prompt Learning is its ability to adapt to new and unseen data with minimal fine-tuning. This is achieved by training the model on a variety of context-specific prompts, which enables it to generalize better across different tasks. For instance, a vision-language model trained with conditional prompts can more accurately interpret and respond to images and questions it has not encountered during training [[127](https://arxiv.org/html/2310.14735v3#bib.bib127)]. This capability is critical for applications such as image captioning, visual question answering, and scene understanding, where the context can vary widely.

Consider an image captioning task where the goal is to generate descriptive captions for images. Using Conditional Prompt Learning, the model can be trained with prompts that are specifically designed for different types of scenes. For example, a prompt for an outdoor scene might include contextual cues related to nature, weather, and activities, while a prompt for an indoor scene might focus on objects, people, and interactions.

In practice, if the model is presented with an image of a bustling market, the conditional prompt might include cues such as ”Identify the types of products being sold,” or ”Describe the interactions between vendors and customers.” This allows the model to generate a caption that is not only accurate but also contextually rich and informative. For instance, the model might produce a caption such as ”Vendors selling fresh fruits and vegetables in a crowded market, with customers browsing and purchasing items” [[121](https://arxiv.org/html/2310.14735v3#bib.bib121)].

The effectiveness of Conditional Prompt Learning has been demonstrated in various studies. [[127](https://arxiv.org/html/2310.14735v3#bib.bib127)] showed that models employing this technique significantly outperform those using static prompts in tasks such as visual question answering and image classification. Furthermore, these models exhibit improved generalization capabilities, making them more robust in real-world applications [[128](https://arxiv.org/html/2310.14735v3#bib.bib128)].

In summary, Conditional Prompt Learning is a powerful methodology that enhances the adaptability and performance of vision-language models by leveraging context-specific prompts. Its ability to dynamically adjust to new and unseen data makes it an invaluable tool for a wide range of applications in multimodal AI.

###  4.4 Modular Prompting

Modular prompting is a sophisticated approach used in vision-language models to handle the complexity of integrating visual and textual data. This method involves breaking down prompts into modular components that can be individually optimized and then recombined to handle complex multimodal tasks. By treating each part of the prompt as a distinct module, the model can better manage and integrate diverse types of information, leading to improved performance and adaptability.

Modular prompting separates the visual and textual components of the input, allowing for independent optimization before combining them. This separation ensures that each modality is processed in the most effective manner, facilitating a more accurate and efficient integration of data [[129](https://arxiv.org/html/2310.14735v3#bib.bib129)]. Additionally, each module can be enhanced with contextual embeddings that provide supplementary information relevant to the task, which helps the model understand the relationships between different pieces of information [[130](https://arxiv.org/html/2310.14735v3#bib.bib130)]. Once the individual modules are processed, the model dynamically integrates the outputs to generate a coherent response, adjusting the integration based on the specific requirements of the task at hand [[131](https://arxiv.org/html/2310.14735v3#bib.bib131)].

Consider an application in open-vocabulary semantic segmentation. In this task, the goal is to segment an image into regions corresponding to different object categories, even if the categories were not seen during training. Using modular prompting, a vision-language model can separately process visual features and textual descriptions of categories.

For instance, the model can be prompted with specific modules for identifying visual features like ”urban environment,” ”vehicles,” and ”pedestrians,” along with textual descriptions for each category. This separation allows the model to optimize the processing of visual and textual data independently. Once the individual modules are processed, the model integrates the results to accurately segment the image and identify the various components, even those it hasn’t seen before [[132](https://arxiv.org/html/2310.14735v3#bib.bib132)].

In conclusion, modular prompting is a powerful method for enhancing the performance and adaptability of vision-language models. By independently optimizing and dynamically integrating visual and textual modules, this approach ensures that models can handle a wide range of tasks with improved accuracy and efficiency.

###  4.5 Zero-shot and Few-shot Prompting

Zero-shot and few-shot prompting are pivotal techniques in the realm of vision-language models, enabling these models to handle tasks with minimal or no task-specific training data. Zero-shot prompting allows models to perform tasks without any specific examples provided during training, relying entirely on their pre-trained knowledge to generalize across new tasks and domains. For example, a model like CLIP can be prompted with a textual description to classify images into categories it has never explicitly been trained on [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)]. On the other hand, few-shot prompting involves providing the model with a small number of examples during inference, significantly enhancing the model’s ability to generalize with limited data [[15](https://arxiv.org/html/2310.14735v3#bib.bib15)].

An illustrative example of these techniques can be seen in the task of Visual Question Answering (VQA). In a zero-shot VQA task, the model might be asked, ”What is the object in the image?” without having seen similar examples during training. The model leverages its broad understanding derived from extensive training on diverse datasets to generate a relevant answer. In a few-shot scenario, the model is provided with a few example questions and answers related to similar images before answering the new question, thus refining its accuracy and contextual understanding [[121](https://arxiv.org/html/2310.14735v3#bib.bib121)].

Research supports the effectiveness of these methods: [[133](https://arxiv.org/html/2310.14735v3#bib.bib133)] explored various prompting techniques for zero- and few-shot learning in vision-language models, demonstrating improved performance on novel tasks. [[15](https://arxiv.org/html/2310.14735v3#bib.bib15)] showed the application of these techniques in CLIP, highlighting the model’s ability to generalize across different domains. Additionally, [[134](https://arxiv.org/html/2310.14735v3#bib.bib134)] presented a method for adapting CLIP to few-shot classification tasks without additional training, emphasizing practical benefits in real-world applications.

In conclusion, zero-shot and few-shot prompting are essential for enhancing the versatility and adaptability of vision-language models. These methods leverage pre-trained knowledge and minimal task-specific examples, enabling models to perform a wide range of tasks with high accuracy and minimal data, thus proving invaluable in the field of multimodal AI.

###  4.6 Reinforcement Learning for Prompt Optimization

Reinforcement Learning (RL) for prompt optimization is an advanced technique designed to enhance the performance of vision-language models by iteratively refining the prompts used during training and inference. This method utilizes the principles of reinforcement learning to navigate the complex parameter space of large models, optimizing the prompts for improved task-specific performance. In RL for prompt optimization, a reward function is defined to evaluate the effectiveness of different prompts based on the model’s output. The model then uses this feedback to adjust and optimize the prompts through a series of iterations, ensuring that the prompts evolve to maximize performance on the target task by leveraging the model’s ability to learn from its interactions with the environment [[135](https://arxiv.org/html/2310.14735v3#bib.bib135)].

Consider the task of Visual Question Answering (VQA), where the goal is to generate accurate answers to questions based on visual input. Using RL for prompt optimization, the model can start with a set of initial prompts and iteratively refine them based on the accuracy of the generated answers. For instance, if the model is asked, ”What is the color of the car in the image?” the initial prompts might produce varied responses. The reward function will assess these responses, favoring prompts that lead to correct answers. Over multiple iterations, the model learns to generate more precise prompts, improving its ability to accurately answer similar questions in the future [[133](https://arxiv.org/html/2310.14735v3#bib.bib133)].

Research supports the effectiveness of these methods. [[135](https://arxiv.org/html/2310.14735v3#bib.bib135)] explored the use of reinforcement learning to optimize discrete text prompts for vision-language models, demonstrating significant improvements in task performance through iterative optimization. [[136](https://arxiv.org/html/2310.14735v3#bib.bib136)] introduced gradient-based techniques to simplify the optimization of hard prompts, providing a robust framework for enhancing vision-language model performance using reinforcement learning. Additionally, [[137](https://arxiv.org/html/2310.14735v3#bib.bib137)] focused on adapting prompts at test time using reinforcement learning, allowing models to dynamically adjust prompts based on real-time feedback, thereby improving adaptability. [[138](https://arxiv.org/html/2310.14735v3#bib.bib138)] discussed a black-box approach to prompt tuning using evolutionary strategies in conjunction with reinforcement learning, highlighting its effectiveness in optimizing prompts for various vision-language tasks.

In conclusion, reinforcement learning for prompt optimization represents a powerful methodology for enhancing the performance and adaptability of vision-language models. By iteratively refining prompts based on task-specific feedback, this approach enables models to achieve higher accuracy and generalization across diverse applications.

###  4.7 Prompt Pattern Catalog

A Prompt Pattern Catalog is an organized collection of prompt templates and patterns designed to enhance the effectiveness of prompt engineering, particularly in vision-language models. This methodology involves creating a standardized set of prompt patterns that can be applied across various tasks, ensuring consistency and optimizing the performance of models through systematic prompt design. By developing a catalog of prompt patterns, researchers and practitioners can ensure a consistent approach to prompt engineering, reducing variability and errors from ad hoc prompt creation [[139](https://arxiv.org/html/2310.14735v3#bib.bib139)]. Using pre-defined prompt patterns streamlines the process of prompt engineering, saving time and resources by allowing practitioners to select and adapt patterns rather than crafting new prompts from scratch [[139](https://arxiv.org/html/2310.14735v3#bib.bib139)]. A well-designed prompt pattern catalog includes patterns for various contexts and applications, enabling models to be quickly adapted to new tasks and domains by selecting the most appropriate patterns [[139](https://arxiv.org/html/2310.14735v3#bib.bib139)]. Systematic use of optimized prompt patterns enhances model performance by providing more effective and contextually appropriate prompts, leading to better task-specific results [[140](https://arxiv.org/html/2310.14735v3#bib.bib140)].

For example, in the task of image captioning, a vision-language model can use standardized patterns tailored for different types of images. For an image of a bustling market, the model could use a prompt pattern designed for urban scenes, including instructions to describe the setting, identify key objects, and note interactions between people. This might result in a detailed and contextually appropriate caption, such as ”The image shows a bustling market with vendors selling various fruits and vegetables, and customers browsing and purchasing items” [[141](https://arxiv.org/html/2310.14735v3#bib.bib141)].

Research supports the effectiveness of prompt pattern catalogs. [[139](https://arxiv.org/html/2310.14735v3#bib.bib139)] outline the development and use of a prompt pattern catalog to improve the effectiveness and efficiency of prompt engineering with vision-language models. [[140](https://arxiv.org/html/2310.14735v3#bib.bib140)] explore how structured prompt patterns can enhance user interaction and improve model outputs in conversational AI, providing insights applicable to vision-language models. [[141](https://arxiv.org/html/2310.14735v3#bib.bib141)] investigate the application of prompt engineering patterns in enterprise settings, demonstrating their utility in optimizing model performance across various tasks. Additionally, [[142](https://arxiv.org/html/2310.14735v3#bib.bib142)] highlight the benefits of using structured prompt patterns in software development, which can be extended to vision-language models.

In conclusion, a prompt pattern catalog is a valuable tool for enhancing the effectiveness and efficiency of prompt engineering in vision-language models. By providing standardized, adaptable, and optimized prompt patterns, this methodology ensures consistent and high-quality model performance across a wide range of tasks.

##  5 Assessing the efficacy of prompt methods

There are many different ways to evaluate the quality of the output. To assess the efficacy of current prompt methods in AIGC tools, evaluation methods can generally be divided into subjective and objective categories.

###  5.1 Subjective and objective evaluations

Subjective evaluations primarily rely on human evaluators to assess the quality of the generated content. Human evaluators can read the text generated by LLMs and score it for quality. Subjective evaluations typically include aspects such as fluency, accuracy, novelty, and relevance [[40](https://arxiv.org/html/2310.14735v3#bib.bib40)]. However, these evaluation methods are, by definition, subjective and can be prone to inconsistencies.

Objective evaluations, also known as automatic evaluation methods, use machine learning algorithms to score the quality of text generated by LLMs. Objective evaluations employ automated metrics, such as BiLingual Evaluation Understudy (BLEU) [[143](https://arxiv.org/html/2310.14735v3#bib.bib143)], which assigns a score to system-generated outputs, offering a convenient and rapid way to compare various systems and monitor their advancements. Other evaluations such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE) [[144](https://arxiv.org/html/2310.14735v3#bib.bib144)], and Metric for Evaluation of Translation with Explicit ORdering (METEOR) [[145](https://arxiv.org/html/2310.14735v3#bib.bib145)], assess the similarity between the generated text and reference text. More recent evaluation methods, such as BERTScore [[146](https://arxiv.org/html/2310.14735v3#bib.bib146)], aim to assess at a higher semantic level. However, these automated metrics often fail to fully capture the assessment results of human evaluators and therefore must be used with caution [[147](https://arxiv.org/html/2310.14735v3#bib.bib147)].

Subjective evaluation and objective evaluation methods each have their own advantages and disadvantages. Subjective evaluation tends to be more reliable than objective evaluation, but it is also more expensive and time-consuming. Objective evaluation is less expensive and quicker than subjective evaluation. For instance, despite numerous pieces of research highlighting the limited correlation between BLEU and alternative metrics based on human assessments, their popularity has remained unaltered [[148](https://arxiv.org/html/2310.14735v3#bib.bib148), [149](https://arxiv.org/html/2310.14735v3#bib.bib149)]. Ultimately, the best way to evaluate the quality of LLM output depends on the specific application [[150](https://arxiv.org/html/2310.14735v3#bib.bib150)]. If quality is the most important factor, then using human evaluators is the better choice. If cost and time are the most important factors, then using automatic evaluation methods is better.

###  5.2 Comparing different prompt methods

In the field of prompt engineering, previous work has mostly focused on designing and optimizing specific prompting methods, but evaluating and comparing different prompting approaches in a systematic manner remains limited. There are some models that are increasingly used to grade the output of other models, which aim to ‘check’ the ability of other models [[151](https://arxiv.org/html/2310.14735v3#bib.bib151), [152](https://arxiv.org/html/2310.14735v3#bib.bib152)]. For instance, LLM-Eval [[153](https://arxiv.org/html/2310.14735v3#bib.bib153)] was developed to measure the open-domain conversations with LLMs. This method tries to evaluate the performance of LLMs on various benchmark datasets [[154](https://arxiv.org/html/2310.14735v3#bib.bib154), [155](https://arxiv.org/html/2310.14735v3#bib.bib155)] and demonstrate their efficiency. Other studies experiment mainly on certain models or tasks and employ disparate evaluation metrics, restricting comparability across methods [[135](https://arxiv.org/html/2310.14735v3#bib.bib135), [156](https://arxiv.org/html/2310.14735v3#bib.bib156)]. Nevertheless, recent research proposed a general evaluation framework called InstructEval [[157](https://arxiv.org/html/2310.14735v3#bib.bib157)] that enables a comprehensive assessment of prompting techniques across multiple models and tasks. The InstructEval study reached the following conclusions: in few-shot settings, omitting prompts or using generic task-agnostic prompts tends to outperform other methods, with prompts having little impact on performance; in zero-shot settings, expert-written task-specific prompts can significantly boost performance, with automated prompts not outperforming simple baselines; the performance of automated prompt generation methods is inconsistent, varying across different models and task types, displaying a lack of generalization. InstructEval provides important references for prompt engineering and demonstrates the need for more universal and reliable evaluation paradigms to design optimal prompts.

##  6 Applications improved by prompt engineering

The output enhancements provided by prompt engineering techniques make LLMs better applicable to real-world applications. This section briefly discusses applications of prompt engineering in fields such as teaching, programming, and others.

![Refer to caption](x3.png) Figure 12: Guideline of courses generated by GPT-4

###  6.1 Assessment in teaching and learning

The study [[158](https://arxiv.org/html/2310.14735v3#bib.bib158)] investigates the application of machine learning methods in young student education. In such a context, prompt engineering can facilitate the creation of personalized learning environments. By offering tailored prompts, LLMs can adapt to an individual’s learning pace and style. Such an approach can allow for personalized assessments and educational content, paving the way for a more individual-centric teaching model. Recent advancements in prompt engineering suggest that AI tools can also cater to students with specific learning needs, thus fostering inclusivity in education [[159](https://arxiv.org/html/2310.14735v3#bib.bib159)]. As a simple example, it is possible for professors to provide rubrics or guidelines for a future course with the assistance of AI. As Figure [12](https://arxiv.org/html/2310.14735v3#S6.F12 "Figure 12 ‣ 6 Applications improved by prompt engineering ‣ Unleashing the potential of prompt engineering: a comprehensive review") shows, when GPT-4 was required to provide a rubric about a course, with a suitable prompt, it was able to respond with a specific result that may satisfy the requirement.

The advancements in prompt engineering also bring better potential for automated grading in education. With the help of sophisticated prompts, LLMs can provide preliminary assessments, reducing the workload for educators while providing instant feedback to students [[160](https://arxiv.org/html/2310.14735v3#bib.bib160)]. Similarly, these models, when coupled with well-designed prompts, can analyze a vast amount of assessment data, thus providing valuable insights into learning patterns and informing educators about areas that require attention or improvement [[161](https://arxiv.org/html/2310.14735v3#bib.bib161), [162](https://arxiv.org/html/2310.14735v3#bib.bib162)].

###  6.2 Content creation and editing

With controllable improved input, LLMs have primarily been used in creative works, such as content creation. Pathways Language Model (PaLM) [[70](https://arxiv.org/html/2310.14735v3#bib.bib70)] and prompting approach have been used to facilitate cross-lingual short story generation [[23](https://arxiv.org/html/2310.14735v3#bib.bib23)]. The Recursive Reprompting and Revision framework (Re3superscriptRe3\mathrm{Re}^{3}roman_Re start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT) [[163](https://arxiv.org/html/2310.14735v3#bib.bib163)] employs zero-shot prompting [[65](https://arxiv.org/html/2310.14735v3#bib.bib65)] with GPT-3 to craft a foundational plan including elements such as settings, characters, and outlines. Subsequently, it adopts a recursive technique, dynamically prompting GPT-3 to produce extended story continuations. For another example, Detailed Outline Control (DOC) [[164](https://arxiv.org/html/2310.14735v3#bib.bib164)] aims at preserving plot coherence across extensive texts generated with the assistance of GPT-3. Unlike Re3superscriptRe3\mathrm{Re}^{3}roman_Re start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT, DOC employs a detailed outliner and detailed controller for implementation. The detailed outliner initially dissects the overarching outline into subsections through a breadth-first method, where candidate generations for these subsections are generated, filtered, and subsequently ranked. This process is similar to the method of chain-of-though (in Section [3.1](https://arxiv.org/html/2310.14735v3#S3.SS1 "3.1 Chain of thought ‣ 3 Advanced methodologies ‣ Unleashing the potential of prompt engineering: a comprehensive review")). Throughout this generation process, an OPT-based Future Discriminators for Generation (FUDGE) [[165](https://arxiv.org/html/2310.14735v3#bib.bib165)] detailed controller plays a crucial role in maintaining relevance.

###  6.3 Computer programming

Prompt engineering can help LLMs perform better at outputting programming codes. By using a self-debugging prompting approach [[60](https://arxiv.org/html/2310.14735v3#bib.bib60)], which contains simple feedback, unit-test, and code explanation prompts module, the text-to-SQL [[166](https://arxiv.org/html/2310.14735v3#bib.bib166)] model is able to provide a solution it can state as correct unless the maximum number of attempts has been reached. Another example, Multi-Turn Programming Benchmark (MTPB) [[167](https://arxiv.org/html/2310.14735v3#bib.bib167)], was constructed to implement a program by breaking it into multi-step natural language prompts.

Another approach is provided in [[168](https://arxiv.org/html/2310.14735v3#bib.bib168)], which introduced the Repo-Level Prompt Generator (RLPG) to dynamically retrieve relevant repository context and construct a prompt for a given task, especially on code auto-completion task. The most suitable prompt is selected by a prompt proposal classifier and combined with the default context to generate the final output.

###  6.4 Reasoning tasks

AIGC tools have shown promising performance in reasoning tasks. Several previous researches found that few-shot prompting can enhance the performance in generating accurate reasoning steps for word-based math problems in the GSM8K dataset [[63](https://arxiv.org/html/2310.14735v3#bib.bib63), [70](https://arxiv.org/html/2310.14735v3#bib.bib70), [58](https://arxiv.org/html/2310.14735v3#bib.bib58), [31](https://arxiv.org/html/2310.14735v3#bib.bib31)]. The strategy of including the reasoning traces in such as few-shot prompts [[49](https://arxiv.org/html/2310.14735v3#bib.bib49)], self-talk [[169](https://arxiv.org/html/2310.14735v3#bib.bib169)] and chain-of-thought [[30](https://arxiv.org/html/2310.14735v3#bib.bib30)], was shown to encourage the model to generate verbalized reasoning steps. [[170](https://arxiv.org/html/2310.14735v3#bib.bib170)] conducted experiments by involving prompting strategies, various fine-tuning techniques, and re-ranking methods to assess their impact on enhancing the performance of a base LLM. They found that a customized prompt significantly improved the model’s ability with fine-tuning, and demonstrated a significant advantage by generating substantially fewer errors in reasoning. In another research, [[65](https://arxiv.org/html/2310.14735v3#bib.bib65)] observed that solely using zero-shot CoT prompting leads to a significant enhancement in the performance of GPT-3 and PaLM when compared to the conventional zero-shot and few-shot prompting methods. This improvement is particularly noticeable when evaluating these models on the MultiArith [[171](https://arxiv.org/html/2310.14735v3#bib.bib171)] and GSM8K [[63](https://arxiv.org/html/2310.14735v3#bib.bib63)] datasets. [[172](https://arxiv.org/html/2310.14735v3#bib.bib172)] also introduced a novel prompting approach called Diverse Verifier on Reasoning Step (DIVERSE). This approach involves using a diverse set of prompts for each question and incorporates a trained verifier with an awareness of reasoning steps. The primary aim of DIVERSE is to enhance the performance of GPT-3 on various reasoning benchmarks, including GSM8K and others. All these works show that in the application of reasoning tasks, properly customized prompts can obtain better results from the model.

###  6.5 Dataset generation

LLMs possess the capability of in-context learning, enabling them to be effectively prompted to generate synthetic datasets for training smaller, domain-specific models. [[173](https://arxiv.org/html/2310.14735v3#bib.bib173)] put forth three distinct prompting approaches for training data generation using GPT-3: unlabeled data annotation, training data generation, and assisted training data generation. Besides, [[174](https://arxiv.org/html/2310.14735v3#bib.bib174)] is designed for the generation of supplementary synthetic data for classification tasks. GPT-3 is utilized in conjunction with a prompt that includes real examples from an existing dataset, along with a task specification. The goal is to jointly create synthetic examples and pseudo-labels using this combination of inputs.

##  7 Exploring and Enhancing LLMs through Insightful Prompt Engineering

Prompt engineering is the process of designing and refining the inputs (prompts) given to LLMs to elicit desired and accurate responses. This technique is crucial not only for optimizing model performance but also for enhancing security. By carefully crafting prompts, researchers and developers can identify and mitigate vulnerabilities in LLMs. Effective prompt engineering can expose weaknesses that might be exploited through adversarial attacks, data poisoning, or other malicious activities. Conversely, poorly designed prompts can inadvertently introduce or exacerbate security risks, making it easier for attackers to manipulate model outputs or extract sensitive information. Thus, prompt engineering serves as both a tool for improving LLM functionality and a critical component of their security framework.

Understanding the interplay between prompt engineering and LLM security is essential for developing robust models that can safely operate in diverse and critical applications. As we delve deeper into the various vulnerabilities and the dual nature of prompt engineering, it becomes clear that maintaining a balance between enhancing capabilities and ensuring security is pivotal for the future of LLMs.

This expanded introduction sets the stage for a detailed exploration of the vulnerabilities inherent in LLMs and the dual-edged nature of prompt engineering, highlighting its significance in both improving performance and safeguarding security.

###  7.1 Introduction to Security in LLMs

The development and deployment of Large Language Models (LLMs) have significantly advanced natural language processing, enabling applications ranging from automated customer support to sophisticated content generation. However, with their increasing integration into critical sectors such as healthcare, finance, and cybersecurity, the importance of ensuring robust security measures for LLMs cannot be overstated. These models, if compromised, can lead to significant breaches of sensitive information and disrupt essential services.

As LLMs become more ubiquitous, their potential vulnerabilities pose severe risks. Adversarial attacks, for instance, can manipulate model outputs, leading to harmful or misleading information dissemination [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)]. Additionally, data poisoning during the training phase can introduce malicious data that corrupts the model’s learning process, resulting in unreliable outputs [[15](https://arxiv.org/html/2310.14735v3#bib.bib15)]. These threats highlight the necessity of comprehensive security protocols to safeguard the integrity and reliability of LLMs.

The increasing reliance on LLMs across various sectors underscores the need for robust security measures. For example, in healthcare, LLMs analyze medical records and provide diagnostic recommendations. A compromised model in this context could lead to incorrect diagnoses and treatment plans, endangering patient lives. Similarly, in finance, LLMs assist in fraud detection and risk assessment; vulnerabilities in these systems could result in significant financial losses and undermine trust in automated financial services [[175](https://arxiv.org/html/2310.14735v3#bib.bib175)]. Therefore, ensuring the security of LLMs is critical to their safe and effective deployment.

###  7.2 Common Vulnerabilities in LLMs

####  7.2.1 Adversarial Attacks

Adversarial attacks involve the deliberate manipulation of input data to deceive a machine learning model into making incorrect predictions. In the context of LLMs, adversarial attacks can take the form of subtly altered prompts or inputs that cause the model to produce unintended or harmful outputs. These attacks exploit the sensitivity of LLMs to small perturbations in input data, revealing significant vulnerabilities [[176](https://arxiv.org/html/2310.14735v3#bib.bib176)]. For instance, an adversarial input might include slight alterations in the text that mislead the model’s natural language understanding capabilities, leading to incorrect or biased responses [[177](https://arxiv.org/html/2310.14735v3#bib.bib177), [7](https://arxiv.org/html/2310.14735v3#bib.bib7)]. The potential for adversarial attacks is particularly concerning in applications such as automated customer service or legal document analysis, where the integrity and accuracy of responses are critical [[178](https://arxiv.org/html/2310.14735v3#bib.bib178)].

Recent research has highlighted various techniques and impacts of adversarial attacks on LLMs. For instance, adversarial demonstration attacks can mislead models into making incorrect predictions even with subtle changes in the input data [[179](https://arxiv.org/html/2310.14735v3#bib.bib179)]. Similarly, methods to automatically generate readable and strategic prompts show the ease with which adversarial examples can be crafted [[180](https://arxiv.org/html/2310.14735v3#bib.bib180)].

Optimization techniques enhance the effectiveness of adversarial attacks, making it more challenging to defend against these threats [[181](https://arxiv.org/html/2310.14735v3#bib.bib181)]. Moreover, a comprehensive overview of these attacks exposes various weaknesses in LLMs, underscoring the critical need for improved security measures [[182](https://arxiv.org/html/2310.14735v3#bib.bib182)].

Real-world examples illustrate the significant impact of adversarial attacks on LLMs. For instance, adversarial inputs in legal document analysis can lead to incorrect legal interpretations, potentially affecting case outcomes. In healthcare, adversarial attacks could mislead models into providing incorrect medical advice, jeopardizing patient safety. These examples highlight the urgent need for effective defenses against adversarial attacks to ensure the safe and reliable deployment of LLMs.

In summary, adversarial attacks pose a significant threat to the security and reliability of LLMs. These attacks exploit the sensitivity of LLMs to small perturbations in input data, leading to unintended or harmful outputs. The role of prompt engineering is crucial in identifying and mitigating these vulnerabilities, ensuring that prompts are carefully crafted to minimize the risk of exploitation. As LLMs become increasingly integrated into critical applications, the need for robust prompt engineering practices becomes ever more pressing. Future research and development should focus on advancing these practices to protect LLMs from emerging threats and ensure their safe deployment.

####  7.2.2 Prompt Hacking

Prompt hacking poses a significant threat to the security and reliability of LLMs. This type of attack involves manipulating the inputs (prompts) given to LLMs to provoke unintended outcomes, ranging from benign errors to malicious activities such as misinformation dissemination and data breaches. Unlike traditional hacking, which typically exploits software vulnerabilities, prompt hacking relies on carefully crafting prompts to deceive the LLM into performing undesired actions [[183](https://arxiv.org/html/2310.14735v3#bib.bib183), [184](https://arxiv.org/html/2310.14735v3#bib.bib184), [185](https://arxiv.org/html/2310.14735v3#bib.bib185), [186](https://arxiv.org/html/2310.14735v3#bib.bib186), [187](https://arxiv.org/html/2310.14735v3#bib.bib187), [188](https://arxiv.org/html/2310.14735v3#bib.bib188)].

Prompt hacking exploits the fundamental way LLMs process and generate responses. Attackers craft malicious prompts designed to exploit the model’s understanding and generation capabilities. These prompts can cause the model to produce harmful outputs, leak sensitive information, or behave erratically. For example, a malicious prompt might subtly alter a request in a way that leads the LLM to generate false or misleading information [[189](https://arxiv.org/html/2310.14735v3#bib.bib189)]. This vulnerability is particularly concerning because it can be executed without the need for sophisticated technical skills. As LLMs become more integrated into various applications, the risk posed by prompt hacking increases, necessitating robust security measures to prevent such attacks [[190](https://arxiv.org/html/2310.14735v3#bib.bib190)].

Understanding prompt hacking is crucial for developing security measures to protect LLM-based applications. Awareness of this vulnerability encourages the implementation of stricter prompt validation and monitoring systems to detect and prevent malicious prompts. By studying prompt hacking techniques, developers can enhance the robustness of LLMs. Implementing countermeasures such as prompt filtering, anomaly detection, and response validation helps mitigate the impact of such attacks and improve overall system security [[191](https://arxiv.org/html/2310.14735v3#bib.bib191)]. Additionally, educating users about the risks of prompt hacking and best practices for secure prompt creation can significantly reduce the likelihood of successful attacks. Training programs and guidelines for creating secure prompts are essential for maintaining the integrity of LLM-based systems [[192](https://arxiv.org/html/2310.14735v3#bib.bib192)].

Recent studies have highlighted the growing threat of prompt hacking and the need for comprehensive security strategies. For example, research published by Rise and Inspire (2024) details various prompt hacking techniques and their implications for LLM security. Similarly, OWASP’s project on LLM prompt hacking provides a framework for understanding and mitigating these vulnerabilities [[193](https://arxiv.org/html/2310.14735v3#bib.bib193)]. Practical implementations of security measures against prompt hacking include the use of advanced monitoring tools that detect suspicious prompt patterns and the integration of machine learning models trained to identify and block malicious prompts. These measures are crucial for ensuring the safe deployment of LLMs in sensitive applications such as healthcare, finance, and customer service [[194](https://arxiv.org/html/2310.14735v3#bib.bib194)].

In conclusion, prompt hacking represents a critical challenge in the secure deployment of LLMs. By understanding the mechanisms and risks associated with this vulnerability, researchers and developers can implement effective security measures to protect against such attacks. Future research should continue to explore innovative solutions to enhance the resilience of LLMs against prompt hacking, ensuring their safe and reliable use in various applications.

####  7.2.3 Threat of Model Stealing

Model stealing attacks pose a significant threat to the security of LLMs. These attacks involve an adversary attempting to replicate the functionality or extract proprietary information from a target model by interacting with it through strategically crafted prompts. The implications of such attacks are severe, as they can lead to intellectual property theft, loss of competitive advantage, and potential misuse of the replicated models [[195](https://arxiv.org/html/2310.14735v3#bib.bib195), [196](https://arxiv.org/html/2310.14735v3#bib.bib196), [197](https://arxiv.org/html/2310.14735v3#bib.bib197)].

Prompts play a crucial role in model stealing attacks. By systematically querying an LLM with carefully designed prompts, an attacker can infer the model’s behavior and underlying parameters. This process, known as ”query-based extraction,” allows the attacker to build a surrogate model that mimics the target model’s responses. The effectiveness of this approach relies on the ability to generate diverse and informative prompts that cover a wide range of inputs the model might encounter [[198](https://arxiv.org/html/2310.14735v3#bib.bib198)].

For instance, an attacker might use a series of varied prompts to understand how the model processes different types of queries. By analyzing the model’s outputs, the attacker can gradually reconstruct the model’s decision-making process and replicate its functionality. This method is particularly effective when the target model is a black-box system, where the attacker has no access to the internal architecture but can observe the outputs generated in response to the inputs [[199](https://arxiv.org/html/2310.14735v3#bib.bib199)].

One notable example of a model stealing attack is the extraction of the projection matrix from OpenAI’s language models. Researchers demonstrated how, through a series of carefully crafted prompts, they could extract significant portions of the model’s architecture and parameters, effectively creating a replica of the original model [[200](https://arxiv.org/html/2310.14735v3#bib.bib200)]. Another incident involved adversaries using prompt engineering techniques to replicate commercial LLMs used in customer service, resulting in substantial intellectual property theft and financial losses for the companies involved [[201](https://arxiv.org/html/2310.14735v3#bib.bib201)].

Numerous studies have explored the effectiveness of model stealing through prompt engineering. For instance, the paper ”Prompt Stealing Attacks Against Large Language Models” highlights how attackers can use advanced prompt engineering techniques to extract high-value information from LLMs [[202](https://arxiv.org/html/2310.14735v3#bib.bib202)]. Another research effort, ”Stealing the Decoding Algorithms of Language Models,” delves into the technical intricacies of how prompts can be exploited to steal decoding algorithms, further showcasing the vulnerability of LLMs to such attacks [[202](https://arxiv.org/html/2310.14735v3#bib.bib202)].

The effectiveness of these attacks underscores the urgent need for robust defenses. Proposed countermeasures include limiting the number of queries a single user can make, implementing anomaly detection to identify suspicious querying patterns, and using defensive perturbations to mislead potential attackers [[203](https://arxiv.org/html/2310.14735v3#bib.bib203)].

####  7.2.4 Backdoor Threats

Backdoor threats involve embedding hidden vulnerabilities within a model that can be activated by specific prompts. These backdoors can be introduced during the training process, often through manipulated training data, and remain dormant until the trigger prompt is presented. For LLMs, a backdoor could be a particular phrase or pattern that, when encountered, causes the model to generate a pre-defined, often malicious output. This poses a significant security risk, as backdoors can be difficult to detect and may be exploited to produce harmful responses or leak sensitive information [[204](https://arxiv.org/html/2310.14735v3#bib.bib204), [205](https://arxiv.org/html/2310.14735v3#bib.bib205), [206](https://arxiv.org/html/2310.14735v3#bib.bib206), [207](https://arxiv.org/html/2310.14735v3#bib.bib207), [208](https://arxiv.org/html/2310.14735v3#bib.bib208), [209](https://arxiv.org/html/2310.14735v3#bib.bib209)]. The covert nature of backdoor threats makes them a formidable challenge, highlighting the need for robust prompt engineering practices to identify and mitigate these vulnerabilities [[210](https://arxiv.org/html/2310.14735v3#bib.bib210)].

Prompt engineering plays a crucial role in both the identification and prevention of these vulnerabilities. Carefully designed prompts can be used to test LLMs for susceptibility to adversarial attacks, data poisoning, and backdoor activations, thus ensuring more secure deployment of these powerful models.

####  7.2.5 Data Poisoning

Data poisoning involves the injection of malicious data into the training set, which compromises the integrity of the model. This type of attack can significantly distort the learning process, leading to erroneous outputs once the model is deployed. In LLMs, data poisoning can be especially insidious as it may go undetected during the training phase. For instance, an attacker might insert misleading or harmful data into the large corpus used to train an LLM, causing the model to learn and reproduce these inaccuracies when prompted. The implications of data poisoning are far-reaching, affecting sectors that rely on accurate data analysis and generation, such as healthcare, finance, and legal services [[211](https://arxiv.org/html/2310.14735v3#bib.bib211)]. Prompt engineering practices must be meticulously monitored to prevent the inadvertent inclusion of poisoned data that could later be exploited.

###  7.3 Prompt Engineering as a Double-Edged Sword

While prompt engineering has significantly enhanced the functionality and versatility of LLMs, it also poses substantial risks if mismanaged. Poorly designed prompts can introduce or exacerbate security vulnerabilities, leading to unintended and potentially harmful outputs [[178](https://arxiv.org/html/2310.14735v3#bib.bib178)]. This dual nature of prompt engineering—capable of both enhancing and compromising LLM performance—highlights its role as a double-edged sword in the deployment of AI technologies. Recent advancements in prompt engineering have demonstrated its power to unlock new capabilities in LLMs, but they also underscore the need for robust and secure design practices. As LLMs become increasingly integrated into critical systems and services, ensuring that prompts are carefully and securely crafted is essential to mitigating risks and maximizing the benefits of these advanced models [[212](https://arxiv.org/html/2310.14735v3#bib.bib212)].

####  7.3.1 Impact of Poorly Designed Prompts on Security

Poorly designed prompts can significantly exacerbate security vulnerabilities in LLMs. Adversarial prompts, for instance, can be crafted to exploit specific weaknesses in the model, leading to unintended or harmful outputs. These vulnerabilities are particularly critical in applications where accuracy and reliability are paramount, such as legal document analysis or automated customer service [[7](https://arxiv.org/html/2310.14735v3#bib.bib7)].

Poorly designed prompts can make LLMs susceptible to various types of attacks, including prompt injection and prompt leaking. Prompt injection attacks involve inserting malicious inputs into prompts to manipulate the model’s output, which can result in the generation of harmful or misleading information. For example, a malicious actor could craft a prompt that subtly alters the model’s response in a way that promotes false information or biases [[178](https://arxiv.org/html/2310.14735v3#bib.bib178)]. Prompt leaking, on the other hand, occurs when sensitive or proprietary information embedded in prompts is exposed, jeopardizing the security and privacy of applications that rely on LLMs [[213](https://arxiv.org/html/2310.14735v3#bib.bib213)].

Prompt engineering plays a crucial role in both mitigating and exacerbating these vulnerabilities. Effective prompt engineering involves designing prompts that minimize the risk of exploitation while maximizing the accuracy and reliability of the model’s outputs. This includes the use of techniques such as input sanitization, context validation, and adversarial training to enhance the model’s robustness against malicious inputs. Conversely, inadequate prompt engineering can inadvertently introduce vulnerabilities, as poorly structured prompts may fail to account for potential security risks or edge cases, leaving the model exposed to exploitation [[211](https://arxiv.org/html/2310.14735v3#bib.bib211)].

In summary, poorly designed prompts can significantly exacerbate security vulnerabilities in LLMs, leading to adverse outcomes such as adversarial attacks, data poisoning, and backdoor threats. The role of prompt engineering is paramount in mitigating these risks, ensuring that prompts are carefully crafted to enhance the model’s security and reliability. As LLMs become increasingly integrated into critical applications, the need for robust and secure prompt engineering practices becomes ever more pressing. Future research and development should focus on advancing these practices to protect LLMs from emerging threats and ensure their safe deployment.

####  7.3.2 Enhancing LLM Security Through Adversarial Training and Robust Prompt Design

Adversarial example generation is a fundamental technique in AI security, designed to test and enhance the robustness of machine learning models. By creating inputs that intentionally mislead models into making incorrect predictions, researchers can identify vulnerabilities and develop strategies to mitigate them. This process is essential for ensuring that models can withstand malicious attacks and function reliably in real-world scenarios [[178](https://arxiv.org/html/2310.14735v3#bib.bib178)]. While direct research on prompt-based adversarial example generation is limited, prompt engineering remains a critical tool in testing model robustness. By designing prompts that subtly alter input data, researchers can simulate adversarial conditions and observe how models respond. For example, ambiguous or misleading prompts can reveal how susceptible a model is to producing biased or incorrect outputs, thereby identifying potential weaknesses [[214](https://arxiv.org/html/2310.14735v3#bib.bib214)].

Adversarial training, which involves training models on adversarial examples, has proven effective in enhancing model robustness. Studies have shown that models exposed to a variety of adversarial inputs during training are better equipped to handle unexpected or malicious data. This method improves the resilience of models against attacks and enhances their overall reliability [[195](https://arxiv.org/html/2310.14735v3#bib.bib195)].

For instance, research has demonstrated that incorporating adversarial examples generated through prompt manipulation can significantly bolster a model’s defenses. By continuously refining prompts and generating diverse adversarial scenarios, researchers can develop comprehensive training datasets that prepare models for a wide range of challenges [[215](https://arxiv.org/html/2310.14735v3#bib.bib215)].

To maximize the effectiveness of adversarial training, integrating robust prompt design is essential. This involves creating prompts that not only test the model’s limits but also enhance its ability to learn from adversarial conditions. Techniques such as mask filling, where portions of text are strategically manipulated, can be used to generate adversarial examples that expose and address vulnerabilities in the model [[181](https://arxiv.org/html/2310.14735v3#bib.bib181)].

In conclusion, while the direct focus on prompt-based adversarial example generation might be limited, the broader application of adversarial training and robust prompt design remains critical in enhancing LLM security. Future research should continue to explore innovative methods for integrating prompt engineering into adversarial training frameworks to ensure the safe and reliable deployment of LLMs.

##  8 Prospective methodologies

Several key developments on the horizon promise to substantially advance prompt engineering capabilities. In the following section, some of the most significant trajectories would be analyzed that are likely to shape the future of prompt engineering. By anticipating where prompt engineering is headed, developments in this field can be proactively steered toward broadly beneficial outcomes.

###  8.1 Better understanding of structures

One significant trajectory about the future of prompt engineering that emerges is the importance of better understanding the underlying structures of AI models. This understanding is crucial to effectively guide these models through prompts and to generate outputs that are more closely aligned with user intent.

At the heart of most AI models, including GPT-4, are complex mechanisms designed to understand and generate human language. The interplay of these mechanisms forms the “structure” of these models. Understanding this structure involves unraveling the many layers of neural networks, the various attention mechanisms at work, and the role of individual nodes and weights in the decision-making process of these models [[216](https://arxiv.org/html/2310.14735v3#bib.bib216)]. Deepening our understanding of these structures could lead to substantial improvements in prompt engineering. The misunderstanding of the model may cause a lack of reproducibility [[217](https://arxiv.org/html/2310.14735v3#bib.bib217)]. By understanding how specific components of the model’s structure influence its outputs, we could design prompts that more effectively exploit these components.

Furthermore, a comprehensive grasp of these structures could shed light on the shortcomings of certain prompts and guide their enhancement. Frequently, the underlying causes for a prompt’s inability to yield the anticipated output are intricately linked to the model’s architecture. For example, [[29](https://arxiv.org/html/2310.14735v3#bib.bib29)] found evidence of limitations in previous prompt models and questioned how much these methods truly understood the model.

Exploration of AI model architectures remains a vibrant research domain, with numerous endeavors aimed at comprehending these sophisticated frameworks. A notable instance is DeepMind’s “Causal Transformer” model [[218](https://arxiv.org/html/2310.14735v3#bib.bib218)], designed to explicitly delineate causal relationships within data. This represents a stride towards a more profound understanding of AI model architectures, with the potential to help us design more efficient prompts.

Furthermore, a more comprehensive grasp of AI model architectures would also yield advancements in explainable AI. Beyond better prompt engineering, this would also foster greater trust in AI systems and promote their integration across diverse industries [[219](https://arxiv.org/html/2310.14735v3#bib.bib219)]. For example, while AI is transforming the financial sector, encompassing areas such as customer service, fraud detection, risk management, credit assessments, and high-frequency trading, several challenges, particularly those related to transparency, are emerging alongside these advancements [[220](https://arxiv.org/html/2310.14735v3#bib.bib220), [221](https://arxiv.org/html/2310.14735v3#bib.bib221)]. Another example is medicine, where AI’s transformative potential faces similar challenges [[222](https://arxiv.org/html/2310.14735v3#bib.bib222), [223](https://arxiv.org/html/2310.14735v3#bib.bib223)].

In conclusion, the trajectory toward a better understanding of AI model structures promises to bring significant advancements in prompt engineering. As we research deeper into these intricate systems, we should be able to craft more effective prompts, understand the reasons behind prompt failures, and enhance the explainability of AI systems. This path holds the potential for transforming how we interact with and utilize AI systems, underscoring its importance in the future of prompt engineering.

###  8.2 Agent for AIGC tools

The concept of AI agents has emerged as a potential trajectory in AI research [[224](https://arxiv.org/html/2310.14735v3#bib.bib224)]. In this brief section, we explore the relationship between agents and prompt engineering and project how agents might influence the future trajectory of AI-generated content (AIGC) tools. By definition, an AI agent comprises large models, memory, active planning, and tool use. AI agents are capable of remembering and understanding a vast array of information, actively planning and strategizing, and effectively using various tools to generate optimal solutions within complex problem spaces [[225](https://arxiv.org/html/2310.14735v3#bib.bib225)].

The evolution of AI agents can be delineated into five distinct phases: models, prompt templates, chains, agents, and multi-agents. Each phase carries its specific implications for prompt engineering. Foundational models, exemplified by architectures such as GPT-4, underpin the realm of prompt engineering.

In particular, prompt templates offer an effective way of applying prompt engineering in practice [[30](https://arxiv.org/html/2310.14735v3#bib.bib30)]. By using these templates, we can create standardized prompts to guide large models, making the generated output more aligned with the desired outcome. The usage of prompt templates is a crucial step towards enabling AI agents to better understand and execute user instructions.

AI agents amalgamate these methodologies and tools into an adaptive framework. Possessing the capability to autonomously modulate their behaviors and strategies, they strive to optimize both efficiency and precision in task execution. A salient challenge for prompt engineering emerges: devising and instituting prompts that adeptly steer AI agents toward self-regulation [[29](https://arxiv.org/html/2310.14735v3#bib.bib29)].

In conclusion, the introduction of agent-based paradigms heralds a novel trajectory for the evolution of AIGC tools. This shift necessitates a reevaluation of established practices in prompt engineering and ushers in fresh challenges associated with the design, implementation, and refinement of prompts.

##  9 Conclusion

In conclusion, prompt engineering has established itself as an essential technique for optimizing the performance of LMs. By employing foundational methods such as clear instructions and iterative refinement, alongside advanced methodologies like Chain of Thought and Self-consistency, the capabilities of LMs are significantly enhanced. For multimodal models, innovative strategies such as Multi-modal Prompt Learning and Context Optimization ensure effective integration and optimization of visual and textual data. The efficacy of these methods is rigorously assessed through both subjective and objective evaluations, confirming their impact across diverse applications, including education, content creation, and programming. Additionally, prompt engineering plays a crucial role in fortifying LM security, identifying vulnerabilities, and mitigating risks through adversarial training and robust prompt design. Looking ahead, future advancements will focus on a deeper understanding of AI model structures and the development of AI agents, further elevating the sophistication and capability of AI systems. This comprehensive review underscores the transformative potential of prompt engineering in advancing AI capabilities, providing a structured framework for future research and applications.

##  10 Acknowledgement

This work was funded by the Natural Science Foundation of China (12271047); Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College (2022B1212010006); UIC research grant (R0400001-22; UICR0400008-21; UICR0700041-22; R72021114); Guangdong College Enhancement and Innovation Program (2021ZDZX1046).

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  * Öztürk [2021] Öztürk D.  What does artificial intelligence mean for organizations? A systematic review of organization studies research and a way forward.  The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume I. 2021;p. 265–289. 
  * Seeamber and Badea [2023] Seeamber R, Badea C.  If our aim is to build morality into an artificial agent, how might we begin to go about doing so?  IEEE Intelligent Systems. 2023;. 





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# Mastering Prompt Engineering: Techniques and Best Practices for Large Language Models

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The designing of prompts has become an indispensable skill necessary for successful interaction with the Large Language Models which include GPT-4 and LLaMA. This article looks at sophisticated approaches and methods that can assist in the crafting of effective prompts that increase the quality, accuracy, and efficiency of the model responses. To this end, the focus will be on the introduction of LLMs, what prompt engineering is about, and provide practical advice accompanied with comprehensive illustrations and references to the latest developments in the area.

# 1\. Introduction

Large Language Models (LLMs) have transformed the field of [natural language processing](https://www.anolytics.ai/natural-language-processing/), allowing machines to generate text that closely mirrors human expression across diverse applications (Brown et al., 2020). As these models continue to evolve in complexity, the art of communicating effectively with them — through prompt engineering — has gained increasing importance. Prompt engineering focuses on designing prompts in ways that align the model’s responses with the user’s goals, enhancing the practical utility of these advanced AI tools (Liu et al., 2021).

This article explores the finer aspects of prompt engineering, offering a comprehensive review of established techniques and emerging best practices. In addition to the standard approaches, we present new prompting methods to broaden the repertoire available to researchers and practitioners working with LLMs.

# 2\. Understanding Large Language Models (LLMs)

LLMs are deep learning models trained on extensive text datasets to predict the next word in a sequence, enabling them to produce coherent, contextually appropriate text (Radford et al., 2019). These models can be divided into two main categories: online and offline models.

## 2.1 Online Models

Online models operate through cloud-based platforms, offering users smooth access without needing substantial local computational power. One advantage of these models is their continuous updates, which ensure that users benefit from the latest advancements in language processing. Notable examples include OpenAI’s GPT-4 (OpenAI, 2023) and Anthropic’s Claude (Bai et al., 2022).

## 2.2 Offline Models

Offline models, by contrast, run directly on local hardware, giving users more control and enhanced privacy. However, this comes with the trade-off of higher computational requirements. Well-known examples include Meta’s LLaMA (Touvron et al., 2023) and Mistral AI’s models (Mistral AI, 2023), which are particularly valuable in scenarios where data sovereignty is a priority.

A primary distinction between LLMs lies in two aspects: their parameter size and context window.

  * **Model Size:** Smaller models, like LLaMA 7B, deliver faster processing times but may struggle with more intricate queries. In contrast, larger models such as LLaMA 65B provide deeper insights but demand greater computational resources (Touvron et al., 2023).
  * **Context Size:** The context window, measured in tokens, defines how much information the model can handle in a single interaction. For instance, GPT-4’s 8192-token window allows it to process large blocks of text efficiently (OpenAI, 2023).



# 3\. What Is Prompt Engineering?

Prompt engineering is both an art and a science, focusing on designing input prompts that effectively communicate the user’s intent to an LLM (Reynolds & McDonell, 2021). By carefully structuring prompts, users can guide the model’s responses to better meet their needs. Key aspects of prompt engineering include:

  * **Enhancing Response Quality:** Using precise language to improve relevance and accuracy.
  * **Minimizing Ambiguity:** Avoiding vague phrasing to reduce the chance of irrelevant outputs.
  * **Unlocking Advanced Capabilities:** Encouraging the model to make full use of its reasoning abilities.
  * **Optimizing Token Usage:** Balancing detail with brevity to maintain efficiency and avoid exceeding token limits.



For example, the prompt _“Summarize the key findings of this research article in bullet points”_ provides clearer guidance than _“Summarize this article,”_ resulting in a more structured response.

# 4\. Key Prompting Techniques

This section explores several effective prompting techniques, each designed to improve user interaction with LLMs.

## 4.1 Zero-Shot Prompting

Zero-shot prompting asks the model a question without additional examples or context. This method relies entirely on the model’s pre-trained knowledge and is ideal for general queries or unbiased responses (Brown et al., 2020).

**Example:**  
_Explain the concept of transfer learning in machine learning._

## 4.2 Few-Shot Prompting

Few-shot prompting provides a few examples to establish the response style or structure before asking the main question (Brown et al., 2020). This technique is useful for tasks requiring specific formatting or domain-specific language.

**Example:**  
_Translate the following English phrases into French:_  
Good morning — Bonjour  
How are you? — Comment ça va?  
See you later — [Model completes]

## 4.3 Chain-of-Thought Prompting

This technique encourages the model to show its reasoning step-by-step, improving the coherence and accuracy of complex answers (Wei et al., 2022). It is especially helpful for problem-solving and mathematical calculations.

**Example:**  
_If a car travels at 60 km/h for 2 hours and then at 80 km/h for 1 hour, what is the total distance traveled? Show your calculations._

## 4.4 Meta Prompting

Meta prompting directs the model to follow specific structures or frameworks, guiding the format rather than the content of the response (Schick & Schütze, 2021).

**Example:**  
_Provide a critical analysis of the following statement using the structure: Introduction, Arguments For, Arguments Against, Conclusion._

## 4.5 Role-Playing

This technique assigns the model a specific persona, helping it generate responses from a particular perspective (Qin et al., 2023).

**Example:**  
_You are an experienced software architect. Explain the principles of microservices architecture to a group of junior developers._

## 4.6 Generated Knowledge Prompting

Here, the model is asked to produce relevant background information before addressing the primary task, leading to more informed responses (Dua et al., 2022).

**Example:**  
_List the key features of renewable energy sources. Then, explain how solar energy can be integrated into the existing power grid._

## 4.7 Self-Critique Prompting

This method encourages the model to evaluate and improve its own outputs, enhancing depth and accuracy (Madaan et al., 2022).

**Example:**  
_Summarize the causes of the French Revolution. Then, critique your summary for any missing key points and revise accordingly._

## 4.8 Contextual Prompting

Providing additional context helps the model tailor responses to specific scenarios or expertise areas (Gao et al., 2021).

**Example:**  
_As a historian specializing in ancient civilizations, describe the significance of the Rosetta Stone in understanding Egyptian hieroglyphs._

## 4.9 Instruction Prompting

Explicit instructions ensure the model follows a specific task structure, improving compliance and output quality (Ouyang et al., 2022).

**Example:**  
_Write a 200-word essay on the impact of climate change on polar bears, citing at least three scientific studies._

## 4.10 Socratic Questioning

Socratic questioning uses a series of probing questions to explore topics in depth, fostering critical analysis (Edward Y. Chang, 2023).

**Example:**  
_Discuss the ethical implications of artificial intelligence: What are the potential benefits? What are the risks? How can we mitigate negative outcomes?_

# 5\. Analyzing and Optimizing Prompts

To achieve optimal results with LLMs, it is essential to continuously analyze and refine prompts. Key strategies include:

## 5.1 Combining Techniques

Combining multiple techniques can improve output quality. For example, using role-playing with chain-of-thought prompting can generate more detailed, context-rich responses.

**Example:**  
_You are a climate scientist (role-playing). Step-by-step (chain-of-thought), explain how greenhouse gases contribute to global warming._

## 5.2 Token Efficiency

Monitoring token usage helps maintain efficiency and prevents exceeding the model’s context limits (OpenAI, 2023). Crafting concise yet clear prompts ensures both cost-effective and high-quality interactions.

## 5.3 Ensuring Contextual Relevance

Aligning prompts with the model’s strengths and training data increases the chances of relevant responses. It’s crucial to provide enough context without overwhelming the model (Liu et al., 2021).

## 5.4 Prompt Iteration

Iterative refinement of prompts helps address ambiguities and improve outcomes. This involves tweaking wording or adding clarifying details based on the model’s previous responses (Reynolds & McDonell, 2021).

# 6\. Applications of Prompt Engineering

Prompt engineering has diverse applications across multiple fields:

  * **Education:** Enhancing student learning and comprehension through tailored prompts.
  * **Research:** Facilitating literature reviews, hypothesis generation, and data analysis.
  * **Business:** Improving customer service, content creation, and strategic planning.
  * **Healthcare:** Supporting diagnostics, patient education, and administrative processes.



# 7\. Challenges and Future Directions

Despite advancements, prompt engineering presents several challenges:

  * **Model Biases:** LLMs may reflect biases inherent in their training data (Bender et al., 2021).
  * **Ambiguity and Misinterpretation:** Poorly structured prompts can lead to unexpected results.
  * **Ethical Considerations:** Ensuring responsible use of AI-generated content remains essential.



Future research should focus on developing standardized frameworks for prompt engineering, improving model interpretability, and addressing biases more effectively.

# References

  * Bai, Y., et al. (2022). _Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback_. arXiv preprint arXiv:2204.05862.
  * Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). _On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?_. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
  * Brown, T. B., et al. (2020). _Language Models are Few-Shot Learners_. Advances in Neural Information Processing Systems, 33, 1877–1901.
  * Dua, D., et al. (2022). _Successive Prompting for Decomposing Complex Questions_.
  * Edward Y. Chang (2023). _Prompting Large Language Models With the Socratic Method_. [arxiv.org/abs/2303.08769](https://arxiv.org/abs/2303.08769).
  * Gao, T., Fisch, A., & Chen, D. (2021). _Making Pre-trained Language Models Better Few-shot Learners_. arXiv preprint arXiv:2012.15723.
  * Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021). _Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing_. arXiv preprint arXiv:2107.13586.
  * Ouyang, L., et al. (2022). _Training Language Models to Follow Instructions with Human Feedback_. Advances in Neural Information Processing Systems, 35, 27730–27744.
  * OpenAI. (2023). _GPT-4 Technical Report_. arXiv preprint arXiv:2303.08774.
  * Qin, L., Ye, S., & Liang, Y. (2023). _Roleplaying as a Service: A Novel Prompting Method for Large Language Models_. arXiv preprint arXiv:2302.08571.
  * Radford, A., et al. (2019). _Language Models are Unsupervised Multitask Learners_. OpenAI Blog, 1(8), 9.
  * Reynolds, L., & McDonell, K. (2021). _Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm_. arXiv preprint arXiv:2102.07350.
  * Schick, T., & Schütze, H. (2021). _It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners_. arXiv preprint arXiv:2009.07118.
  * Touvron, H., et al. (2023). _LLaMA: Open and Efficient Foundation Language Models_. arXiv preprint arXiv:2302.13971.
  * Wei, J., et al. (2022). _Chain-of-Thought Prompting Elicits Reasoning in Large Language Models_. arXiv preprint arXiv:2201.11903.



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[Blog](https://invedus.com/blog/) » An Overview of Prompt Engineering - The Art and Science of It

![Prompt Engineering](https://invedus.com/blog/wp-content/uploads/2023/11/Prompt-Engineering-3.jpg)

#  An Overview of Prompt Engineering - The Art and Science of It 

Updated on - November 17th, 2023, [   Garima ](https://invedus.com/blog/author/garima-invedus/)

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Through this article, we will shed light on **[prompt engineering](https://invedus.com/services/hire-prompt-engineers/)** today. 

However, have you ever wondered how you can read all of this? You were first given an introduction to the 26 alphabet by your teacher, who then gave you practice using them. As such, you are proficient in reading and comprehending the idea when utilizing these 26 alphabets.

Ever wonder how much you can accomplish with just these 26 characters? Actually, your teacher gives you instructions at that point on how to handle these characters in a different manner. At that time, she trained your brain using **prompt engineering**.   

Let us have some fun and discover how you can get an improvised result with minimal error and maximum accuracy by simply giving your chatbot a few commands. Prior to anything else, learn what prompt engineering is.

Things to Talk About Inside

  * A brief introduction to prompt engineering 
    * A brief definition of prompt engineering and its goals
    * Examples of prompt engineering in use
  * Why is prompt engineering trending?
  * Why is prompt engineering important? 
    * The benefits of prompt engineering, such as its ability to improve the performance of large language models and make them more versatile,
  * What are the different types of prompts? 
    * A high-level overview of the different types of prompts, such as open-ended prompts, closed-ended prompts, and generative prompts
  * Why does today's world require prompt engineering?
  * Types of Prompt Engineering
  * What are the benefits and challenge? 
    * A summary of the pros and cons of prompt engineering, such as its potential for misuse but also its potential to democratize access to AI.
  * Main ideas to keep in mind while designing prompts for AI models



## What is Prompt engineering?

Prompt engineering is preparing step-by-step instructions for different topics you are crafting so that you can achieve better results from the large language models (LLMs). 

LLM is an artificial intelligence system that has been trained to respond to queries after compiling a variety of data sets. After that, it will react to you in accordance with the cues you gave it and assist you in completing tasks like generating text, translating languages, and writing different kinds of creative content. However, LLMs can only generate outputs that are consistent with the prompts they are given

Today, we are the generation that sees the evolution and emergence of AI. Artificial intelligence is not a new buzzword for anyone these days. Almost everyone uses chatbots, Chatgpt, and Google Bard to get answers to their questions or to translate their feelings into words. Even though Chatgpt and Bard, our deep learning models and chatbots, are incredibly skilled at answering your questions, there are instances when we have noticed that these LLMs are unable to provide an accurate response to any of our queries. 

These LLMs are not accurately answering your query because they are not receiving the exact commands.

## Why is prompt engineering trending?

Professionals with the ability to design effective prompts to guide large language models (LLMs) are in greater demand as these models become more and more popular in a range of applications.

LLMs have demonstrated their extraordinary abilities and adaptability beyond writing and generating text to translating languages, as demonstrated by Chatgpt, Bard, and others. By providing prompts, you open up these LLMs to a wider audience and allow people who do not know much about AI to use them for a variety of purposes. 

The need for AI-powered solutions is expanding like the universe across all domains. Many small and large businesses are using artificial intelligence to enhance decision-making, customer service, and process automation. 

Reports by **Gartner** indicate that up to 80% of organizations will incorporate AI by 2025. This surge in AI adoption fuels the need for prompt engineers who can develop and deploy AI-powered solutions, ensuring organizational goals.

Even though this is a relatively new industry, people are embracing it quickly. But you must ensure some facts before using it. See the following for advice on using prompt engineering.

## Examples Where Prompt Engineering Can Be Used

Here are a few instances of prompt engineering that you can apply to obtain a more genuine and adaptable response. 

**1\. To Train the LLM** : By using prompt engineering, engineers can train LLMs to generate realistic dialogue for chatbots that is relevant to the context of the conversation and that sounds natural and engaging.

**2\. To Improve Accuracy:** Prompt engineering can be used to improve the accuracy and fluency of machine translation. For instance, the engineer can assist the LLM in producing a translation that is more accurate and sounds more natural by giving the LLM information regarding the translation's context.

**3\. Question answering:** Prompt engineering can be used to improve the accuracy and informativeness of question-answering models. For example, a prompt engineer might provide the model with additional context about the question, such as the topic area or the source of the question. Or, a prompt engineer might use specific keywords or phrases to encourage the model to explore a particular topic or style. For example, to make a question-answering model more likely to provide a factual answer, a prompt engineer might use the phrase "according to the facts" in the prompt.

**4\. Code generation** : Prompt engineering can be used to improve the efficiency and accuracy of code generation models. For example, a prompt engineer might provide the model with a template or skeleton of the code that it needs to generate. Or, a prompt engineer might use specific keywords or phrases to encourage the model to use a particular programming language or coding style. For example, to make a code generation model more likely to generate Python code, a prompt engineer might use the phrase "in Python" in the prompt.

**5\. Creative writing:** Prompt engineering can be used to improve the creativity and fluency of creative writing models. For example, a prompt engineer might provide the model with a list of keywords or phrases to include in the output. Or, a prompt engineer might use specific keywords or phrases to encourage the model to explore a particular genre or style. For example, to make a creative writing model more likely to generate a poem, a prompt engineer might use the phrase "write a poem about" in the prompt.

**6 Translation** : Prompt engineering can be used to improve the accuracy and fluency of translation models. For example, a prompt engineer might provide the model with additional context about the text being translated, such as the genre or the target audience. Or, a prompt engineer might use specific keywords or phrases to encourage the model to use a particular translation style. For example, to make a translation model more likely to generate a formal translation, a prompt engineer might use the phrase "translate into formal English" in the prompt.

**7\. Summarization:** Prompt engineering can be used to improve the accuracy and informativeness of summarization models. For example, a prompt engineer might provide the model with a list of key points to include in the summary. Or, a prompt engineer might use specific keywords or phrases to encourage the model to summarize the text in a particular style. For example, to make a summarization model more likely to generate a bullet point summary, a prompt engineer might use the phrase "summarize the following key points in a bullet point list" in the prompt.

##  Some Examples of how to use Prompt Engineering

Here are some examples where you can use prompt engineering:

### 1\. Text completion

**Prompt:** Incomplete text:

The new artificial intelligence system is so powerful that it can ____.

**Possible completions**

  * translate languages
  * write different kinds of creative content
  * answer your questions in an informative way
  * generate code
  * drive a car



The most likely completion is “**answer your questions in an informative way** ," because it is the most common task that AI systems are used for. 

### 2\. Instruction-based

**Prompt:** Translate the following sentence into Spanish: "I am a large language model."

**Output** : Soy un gran modelo lingüístico.

### 3\. Role prompt

**Prompt** : As a customer support representative, please help me troubleshoot an issue I am having with my internet connection.

**Output:** Hi, I'm happy to help you troubleshoot your internet connection. Can you tell me more about the issue you're experiencing?

### 4\. Chain-of-thought prompting

**Prompt:**

Read the following sentence: "The cat sat on the mat."

Identify the subject of the sentence.

What is the subject's action?

What is the subject's location?

**Output:**

The cat sat on the mat.

The subject is "cat."

The subject's action is "sat."

The subject's location is "on the mat."

### 5\. Structured output

**Prompt** : Generate a list of the top 10 programming languages, sorted by popularity.

**Output:** Python

JavaScript

Java

C/C++

C#

PHP

Go

R

Swift

Ruby

### 6\. Question Answering

**Prompt:** What is 2+2?

**Output:** 4

**Prompt:** Tell me the sum of the first 10 natural numbers

**Output:** 55

### 7\. Information Extraction

**Input text:**

Invoice:

Invoice number: 12345

Customer name: John Doe

Customer address: 123 Main Street, Anytown, CA 91234

Item description: 1 laptop

Price: $1000

Total: $1000

**Extracted information:**

  * Entity type: Person
  * Entity name: John Doe
  * Entity type: Organization
  * Entity name: Company Name
  * Entity type: Address
  * Entity name: 123 Main Street, Anytown, CA 91234
  * Entity type: Product
  * Entity name: Laptop
  * Entity type: Price
  * Entity value: $1000



Prompt engineering can also be used to enhance the efficacy of large language models on a variety of tasks, such as text classification, conversation, text summarization, code generation, and reasoning.

## Goals of Prompt Engineering

The goals of prompt engineering are elaborated below

  1. **Improve the performance of AI models** : Prompt engineering can help to improve the accuracy, fluency, and creativity of AI models on a wide range of tasks, such as question answering, code generation, creative writing, translation, and summarization.
  2. **Boost the effectiveness of AI models:** Prompt engineering can help cut down on the quantity of data and training time needed to train AI models.
  3. **Increase the adaptability of AI models:** Prompt engineering can allow AI models to execute new tasks without requiring retraining.
  4. **Make AI models more interpretable:** Prompt engineering can help to make AI models more explainable and transparent, which can be important for building trust in AI systems.
  5. **Increase AI Predictability:** Prompt engineering can be used to increase the predictability of large language models by feeding them more data and syncing that data with real-world scenarios and situations. 
  6. **Reduce the need for manual fine-tuning of LLMs:** Prompts can be used to adapt LLMs to specific tasks or domains without having to modify the underlying model architecture or training data.



worth Exploring: [Prompt Engineer Job Description Template](https://invedus.com/blog/prompt-engineer-job-description-template/)

## Why does today's world require prompt engineering?

These days, we have resources at our disposal, but knowing how to use them optimally can vary with a few simple guidelines. 

Here's a simple example to help you grasp this idea, if you search for any unethical item on Google, it will not display the results you need. However, if you tell it that you need the information for educational purposes, it will display the results you need based on your educational background.

Consider the following example. If you search for the circumference of a circle on LLM, you will find that C = 2 \pi r. But when you give it the required prompts, it can tell you each and every aspect of the circumference with real-life examples. 

The next example you can think of involves several organizations. Teachers can create individualized learning materials and assessments by creating powerful prompts, which will improve student engagement and knowledge acquisition in an increasingly digital learning environment.

Furthermore, in the current context, prompt engineering is required for several reasons. Check out below

  * **To harness the power of large language models (LLMs)** : LLMs are a new type of AI model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, LLMs are still under development, and they can produce inaccurate or irrelevant outputs if they are not carefully guided. Prompt engineering is the process of designing and optimizing prompts to guide LLMs to generate desired outputs.
  * **To democratize access to AI:**   Prompt engineering makes it easier for people without a background in computer science or machine learning to use LLMs. This is important because LLMs have the potential to revolutionize many different industries and applications.
  * **To develop new and innovative AI applications:** LLMs are still a relatively new technology, and their full potential has not yet been realized. Prompt engineering is a key tool for exploring the capabilities of LLMs and discovering new ways to use them.



## Types of Prompt Engineering

There are many different types of prompt engineering techniques, but some of the most common include:

![](https://invedus.com/blog/wp-content/uploads/2023/10/Types-of-Prompt-Engineering-1-1024x512.jpg)

  1. **N-shot prompting:** This technique involves providing the LLM with a few examples (N-shots) of the desired output before asking it to generate its own output. This can be used for a variety of tasks, such as translation, summarization, and code generation.
  2. **Chain-of-thought (CoT) prompting:** This technique involves breaking down a complex task into a series of smaller, simpler tasks. The LLM is then instructed to complete each task sequentially, with each task's output serving as the input for the subsequent task. This can be used for tasks such as question-answering and problem-solving.
  3. **Generated knowledge prompting:** This technique involves generating prompts that contain additional knowledge or information that the LLM may not have been trained on. This can be used to improve the accuracy and completeness of the LLM's output.
  4. **Positive and negative prompting** : This technique involves using both positive prompts (prompts that encourage the LLM to generate certain types of output) and negative prompts (prompts that discourage the LLM from generating certain types of output). This can be used to control the style and tone of the LLM's output, as well as to prevent it from generating harmful or offensive content.
  5. **Interactive context-aware prompting:** This technique involves iteratively refining the prompt based on the LLM's output. This can be used to help the LLM understand and respond to complex or ambiguous prompts.
  6. **Role-playing prompting:** This technique involves instructing the LLM to take on a specific role or identity when generating its output. This can be used to generate more creative and engaging outputs, such as poems, stories, and scripts.
  7. **Multiple Choice Question (MCQ) Prompting:** This method consists of giving the LLM several options for a single question, with the instruction to choose the correct answer from the list.  



## Benefits of Prompt Engineering

In terms of its immediate advantages, we have observed how AI has raised our standard of living in the present day. Rest also helps in the following aspects

**Boost LLM Performance**

The market for prompt engineering is currently relatively small, but it will soon reach its peak**.** According to a recent report by Grand View Research, the global prompt engineering market is expected to reach $1.4 billion by 2028, growing at a CAGR of 35.2% from 2023 to 2028. Furthermore, it is utilized for forecasting in various domains such as meteorology, healthcare, academia, and so forth. We can now conclude that, although it was originally designed primarily for textual purposes, it is now also being used for prediction.

**Increased relevance and accuracy**

Well-designed prompts can assist LLMs in producing outputs that are more pertinent and accurate, particularly for difficult or complex tasks.

**Enhanced productivity**

Prompt engineering can assist in cutting down on the time and materials needed to train and implement LLMs.

**Enhanced control and flexibility**

Prompt engineering gives users more control over the outputs of LLMs and allows them to tailor LLMs to specific needs and tasks.

**Reduced bias**

Prompt engineering can help to reduce bias in LLMs by providing them with more explicit guidance and context.

**Extensions**

Prompt engineering can assist in opening up new possibilities for LLMs, like producing original content or carrying out operations not designed for the model.

_Related:[The Pitfalls of Using AI in UI Design](https://invedus.com/blog/why-you-shouldnt-try-to-create-ui-with-ai/)_

## Cons of Prompt Engineering

Saying that prompt engineering is error-free is a challenging task. It has multiple disadvantages as well. For instance,

  * **Complexity Increases while Offering Prompts:** Providing prompts for engineering can be difficult and complex, requiring in-depth technical knowledge and strong problem-solving abilities. This can make it difficult for non-experts to use prompt engineering effectively.
  * **Takes Time and Resources:** When dealing with complex tasks, prompt engineering can be a time- and resource-intensive process. This can limit its scalability and applicability in real-world settings.
  * **Shows Bias Result:** Prompt engineering can inadvertently introduce bias into LLMs, especially if the prompts are not carefully designed. This could lead to LLMs producing outputs that are discriminatory or unfair.
  * **Security:** Prompt engineering could be used to exploit LLMs for malicious purposes, such as generating fake news or disinformation. It is important to develop safeguards and security measures to mitigate these risks.



## Main ideas to keep in mind while designing prompts for AI models

1\. **Be specific** : First and foremost, users should remember that when interacting with the AI model, they must be extremely specific. The more specific you are in your prompt, the better the model will be able to understand what you want it to do. For example, instead of saying Write a story, you could say Write a science fiction story about a group of astronauts who discover a new planet.

2\. **Be succinct and clear:** Do not use ambiguous, baffled, or perplexing wording in your prompt. It is important to keep in mind that the commands you are giving are directed towards a machine, not a person. It does not have its own memory to understand. It is important to emphasize that the model must comprehend precisely what you are asking of it.

3.**Provide context:** The model will be better able to produce pertinent and educational outputs the more context you can give it. For example, if you are asking the model to translate a sentence, you should provide it with the original language and the target language.

4\. **Consider the format of the output:** Do you want the model to generate text, code, an image, or something else? Be sure to specify the desired format in your prompt.

5\. **Give examples (If Possible):** You can help the model learn what you are looking for by giving examples of the desired output.**** For example, if you are asking the model to generate a product review, you could provide it with examples of positive and negative reviews.

6\. **Get feedback before asking for Final Output:** Once the model has generated an output, review it and provide feedback. This will support the model's ability to grow over time and learn from its errors.

## Conclusion

In this write-up, we have practically discussed every facet of prompt engineering. what makes it popular. We also talked about how LLMs are becoming more potent and adaptable and how there will be an increasing need for prompt engineers. 

If you are interested in harnessing the power of AI, prompt engineering is an essential skill to master.

As AI adoption continues to grow, the demand for prompt engineers is on the rise, making this trend increasingly important. 

Even though prompt engineering is a relatively new feature for the market, users who want to use it effectively should be clear and precise, provide context, and provide feedback for improvement.

On the other hand, you can hire a Prompt engineer for your company if you encounter any difficulties analyzing this technique.

[![CTA](https://invedus.com/blog/wp-content/uploads/2023/11/Hire-Prompt-Engineering-1.jpg)](https://invedus.com/services/hire-prompt-engineers/)

 

About Garima

![](https://secure.gravatar.com/avatar/946e5cb551d94aa6b5b9ae6089033688?s=90&d=mm&r=g)Meet Garima, an integral member of the Invedus editorial team. With three years of experience in crafting compelling narratives, she brings a wealth of expertise to our roster. Her mastery of technical content writing ensures clear and precise communication. Discover how she can elevate your brand's story with persuasive and captivating content.

[View all posts by Garima](https://invedus.com/blog/author/garima-invedus/) | [___](https://www.linkedin.com/in/garimasaxena6/)|[__](https://twitter.com/GarimaS70686264)|[__](https://www.tumblr.com/invedus)

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####  [ Garima ](https://invedus.com/blog/author/garima-invedus/)

Meet Garima, an integral member of the Invedus editorial team. With three years of experience in crafting compelling narratives, she brings a wealth of expertise to our roster. Her mastery of technical content writing ensures clear and precise communication. Discover how she can elevate your brand's story with persuasive and captivating content. 

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<title>Universal Imitation Games</title>

<link>https://arxiv.org/abs/2405.01540</link>

<description>arXiv:2405.01540v1 Announce Type: new 

Abstract: Alan Turing proposed in 1950 a framework called an imitation game to decide if a machine could think. Using mathematics developed largely after Turing -- category theory -- we analyze a broader class of universal imitation games (UIGs), which includes static, dynamic, and evolutionary games. In static games, the participants are in a steady state. In dynamic UIGs, "learner" participants are trying to imitate "teacher" participants over the long run. In evolutionary UIGs, the participants are competing against each other in an evolutionary game, and participants can go extinct and be replaced by others with higher fitness. We use the framework of category theory -- in particular, two influential results by Yoneda -- to characterize each type of imitation game. Universal properties in categories are defined by initial and final objects. We characterize dynamic UIGs where participants are learning by inductive inference as initial algebras over well-founded sets, and contrast them with participants learning by conductive inference over the final coalgebra of non-well-founded sets. We briefly discuss the extension of our categorical framework for UIGs to imitation games on quantum computers.</description>

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<dc:creator>Sridhar Mahadevan</dc:creator>

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<title>Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications</title>

<link>https://arxiv.org/abs/2405.01585</link>

<description>arXiv:2405.01585v1 Announce Type: new 

Abstract: In recent times Large Language Models have exhibited tremendous capabilities, especially in the areas of mathematics, code generation and general-purpose reasoning. However for specialized domains especially in applications that require parsing and analyzing large chunks of numeric or tabular data even state-of-the-art (SOTA) models struggle. In this paper, we introduce a new approach to solving domain-specific tabular data analysis tasks by presenting a unique RAG workflow that mitigates the scalability issues of existing tabular LLM solutions. Specifically, we present Tabular Embedding Model (TEM), a novel approach to fine-tune embedding models for tabular Retrieval-Augmentation Generation (RAG) applications. Embedding models form a crucial component in the RAG workflow and even current SOTA embedding models struggle as they are predominantly trained on textual datasets and thus underperform in scenarios involving complex tabular data. The evaluation results showcase that our approach not only outperforms current SOTA embedding models in this domain but also does so with a notably smaller and more efficient model structure.</description>

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<arxiv:announce_type>new</arxiv:announce_type>

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<dc:creator>Sujit Khanna, Shishir Subedi</dc:creator>

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<item>

<title>Large Language Models for UAVs: Current State and Pathways to the Future</title>

<link>https://arxiv.org/abs/2405.01745</link>

<description>arXiv:2405.01745v1 Announce Type: new 

Abstract: Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.</description>

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<dc:creator>Shumaila Javaid, Nasir Saeed, Bin He</dc:creator>

</item>

<item>

<title>Learning under Imitative Strategic Behavior with Unforeseeable Outcomes</title>

<link>https://arxiv.org/abs/2405.01797</link>

<description>arXiv:2405.01797v1 Announce Type: new 

Abstract: Machine learning systems have been widely used to make decisions about individuals who may best respond and behave strategically to receive favorable outcomes, e.g., they may genuinely improve the true labels or manipulate observable features directly to game the system without changing labels. Although both behaviors have been studied (often as two separate problems) in the literature, most works assume individuals can (i) perfectly foresee the outcomes of their behaviors when they best respond; (ii) change their features arbitrarily as long as it is affordable, and the costs they need to pay are deterministic functions of feature changes. In this paper, we consider a different setting and focus on imitative strategic behaviors with unforeseeable outcomes, i.e., individuals manipulate/improve by imitating the features of those with positive labels, but the induced feature changes are unforeseeable. We first propose a Stackelberg game to model the interplay between individuals and the decision-maker, under which we examine how the decision-maker's ability to anticipate individual behavior affects its objective function and the individual's best response. We show that the objective difference between the two can be decomposed into three interpretable terms, with each representing the decision-maker's preference for a certain behavior. By exploring the roles of each term, we further illustrate how a decision-maker with adjusted preferences can simultaneously disincentivize manipulation, incentivize improvement, and promote fairness.</description>

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<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang</dc:creator>

</item>

<item>

<title>Non-linear Welfare-Aware Strategic Learning</title>

<link>https://arxiv.org/abs/2405.01810</link>

<description>arXiv:2405.01810v1 Announce Type: new 

Abstract: This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future data. Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy. Instead, this work focuses on general non-linear settings where agents respond to the decision policy with only "local information" of the policy. Moreover, we simultaneously consider the objectives of maximizing decision-maker welfare (model prediction accuracy), social welfare (agent improvement caused by strategic behaviors), and agent welfare (the extent that ML underestimates the agents). We first generalize the agent best response model in previous works to the non-linear setting, then reveal the compatibility of welfare objectives. We show the three welfare can attain the optimum simultaneously only under restrictive conditions which are challenging to achieve in non-linear settings. The theoretical results imply that existing works solely maximizing the welfare of a subset of parties inevitably diminish the welfare of the others. We thus claim the necessity of balancing the welfare of each party in non-linear settings and propose an irreducible optimization algorithm suitable for general strategic learning. Experiments on synthetic and real data validate the proposed algorithm.</description>

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<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Tian Xie, Xueru Zhang</dc:creator>

</item>

<item>

<title>SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning</title>

<link>https://arxiv.org/abs/2405.01839</link>

<description>arXiv:2405.01839v1 Announce Type: new 

Abstract: Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks. However, most of the multi-agent systems cannot easily handle them, due to the complexity of the state and task space. The social impact theory regards the complex influencing factors as forces acting on an agent, emanating from the environment, other agents, and the agent's intrinsic motivation, referring to the social force. Inspired by this concept, we propose a novel gradient-based state representation for multi-agent reinforcement learning. To non-trivially model the social forces, we further introduce a data-driven method, where we employ denoising score matching to learn the social gradient fields (SocialGFs) from offline samples, e.g., the attractive or repulsive outcomes of each force. During interactions, the agents take actions based on the multi-dimensional gradients to maximize their own rewards. In practice, we integrate SocialGFs into the widely used multi-agent reinforcement learning algorithms, e.g., MAPPO. The empirical results reveal that SocialGFs offer four advantages for multi-agent systems: 1) they can be learned without requiring online interaction, 2) they demonstrate transferability across diverse tasks, 3) they facilitate credit assignment in challenging reward settings, and 4) they are scalable with the increasing number of agents.</description>

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<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<dc:creator>Qian Long, Fangwei Zhong, Mingdong Wu, Yizhou Wang, Song-Chun Zhu</dc:creator>

</item>

<item>

<title>An Essay concerning machine understanding</title>

<link>https://arxiv.org/abs/2405.01840</link>

<description>arXiv:2405.01840v1 Announce Type: new 

Abstract: Artificial intelligence systems exhibit many useful capabilities, but they appear to lack understanding. This essay describes how we could go about constructing a machine capable of understanding. As John Locke (1689) pointed out words are signs for ideas, which we can paraphrase as thoughts and concepts. To understand a word is to know and be able to work with the underlying concepts for which it is an indicator. Understanding between a speaker and a listener occurs when the speaker casts his or her concepts into words and the listener recovers approximately those same concepts. Current models rely on the listener to construct any potential meaning. The diminution of behaviorism as a psychological paradigm and the rise of cognitivism provide examples of many experimental methods that can be used to determine whether and to what extent a machine might understand and to make suggestions about how that understanding might be instantiated.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01840v1</guid>

<category>cs.AI</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Herbert L. Roitblat</dc:creator>

</item>

<item>

<title>Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization</title>

<link>https://arxiv.org/abs/2405.01906</link>

<description>arXiv:2405.01906v1 Announce Type: new 

Abstract: The neural combinatorial optimization (NCO) approach has shown great potential for solving routing problems without the requirement of expert knowledge. However, existing constructive NCO methods cannot directly solve large-scale instances, which significantly limits their application prospects. To address these crucial shortcomings, this work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural combinatorial optimization. In particular, we design a powerful yet lightweight instance-conditioned adaptation module for the NCO model to generate better solutions for instances across different scales. In addition, we develop an efficient three-stage reinforcement learning-based training scheme that enables the model to learn cross-scale features without any labeled optimal solution. Experimental results show that our proposed method is capable of obtaining excellent results with a very fast inference time in solving Traveling Salesman Problems (TSPs) and Capacitated Vehicle Routing Problems (CVRPs) across different scales. To the best of our knowledge, our model achieves state-of-the-art performance among all RL-based constructive methods for TSP and CVRP with up to 1,000 nodes.</description>

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<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang</dc:creator>

</item>

<item>

<title>Model-based reinforcement learning for protein backbone design</title>

<link>https://arxiv.org/abs/2405.01983</link>

<description>arXiv:2405.01983v1 Announce Type: new 

Abstract: Designing protein nanomaterials of predefined shape and characteristics has the potential to dramatically impact the medical industry. Machine learning (ML) has proven successful in protein design, reducing the need for expensive wet lab experiment rounds. However, challenges persist in efficiently exploring the protein fitness landscapes to identify optimal protein designs. In response, we propose the use of AlphaZero to generate protein backbones, meeting shape and structural scoring requirements. We extend an existing Monte Carlo tree search (MCTS) framework by incorporating a novel threshold-based reward and secondary objectives to improve design precision. This innovation considerably outperforms existing approaches, leading to protein backbones that better respect structural scores. The application of AlphaZero is novel in the context of protein backbone design and demonstrates promising performance. AlphaZero consistently surpasses baseline MCTS by more than 100% in top-down protein design tasks. Additionally, our application of AlphaZero with secondary objectives uncovers further promising outcomes, indicating the potential of model-based reinforcement learning (RL) in navigating the intricate and nuanced aspects of protein design</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01983v1</guid>

<category>cs.AI</category>

<category>q-bio.BM</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Frederic Renard, Cyprien Courtot, Alfredo Reichlin, Oliver Bent</dc:creator>

</item>

<item>

<title>A semantic loss for ontology classification</title>

<link>https://arxiv.org/abs/2405.02083</link>

<description>arXiv:2405.02083v1 Announce Type: new 

Abstract: Deep learning models are often unaware of the inherent constraints of the task they are applied to. However, many downstream tasks require logical consistency. For ontology classification tasks, such constraints include subsumption and disjointness relations between classes.

In order to increase the consistency of deep learning models, we propose a semantic loss that combines label-based loss with terms penalising subsumption- or disjointness-violations. Our evaluation on the ChEBI ontology shows that the semantic loss is able to decrease the number of consistency violations by several orders of magnitude without decreasing the classification performance. In addition, we use the semantic loss for unsupervised learning. We show that this can further improve consistency on data from a distribution outside the scope of the supervised training.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.02083v1</guid>

<category>cs.AI</category>

<category>cs.LO</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://creativecommons.org/publicdomain/zero/1.0/</dc:rights>

<dc:creator>Simon Fl\"ugel, Martin Glauer, Till Mossakowski, Fabian Neuhaus</dc:creator>

</item>

<item>

<title>Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph</title>

<link>https://arxiv.org/abs/2405.02105</link>

<description>arXiv:2405.02105v1 Announce Type: new 

Abstract: Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.02105v1</guid>

<category>cs.AI</category>

<category>cs.CL</category>

<category>cs.IT</category>

<category>math.IT</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-sa/4.0/</dc:rights>

<dc:creator>Vladyslav Nechakhin, Jennifer D'Souza, Steffen Eger</dc:creator>

</item>

<item>

<title>Towards a Formal Creativity Theory: Preliminary results in Novelty and Transformativeness</title>

<link>https://arxiv.org/abs/2405.02148</link>

<description>arXiv:2405.02148v1 Announce Type: new 

Abstract: Formalizing creativity-related concepts has been a long-term goal of Computational Creativity. To the same end, we explore Formal Learning Theory in the context of creativity. We provide an introduction to the main concepts of this framework and a re-interpretation of terms commonly found in creativity discussions, proposing formal definitions for novelty and transformational creativity. This formalisation marks the beginning of a research branch we call Formal Creativity Theory, exploring how learning can be included as preparation for exploratory behaviour and how learning is a key part of transformational creative behaviour. By employing these definitions, we argue that, while novelty is neither necessary nor sufficient for transformational creativity in general, when using an inspiring set, rather than a sequence of experiences, an agent actually requires novelty for transformational creativity to occur.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.02148v1</guid>

<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>new</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Lu\'is Esp\'irito Santo, Geraint Wiggins, Am\'ilcar Cardoso</dc:creator>

</item>

<item>

<title>Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R</title>

<link>https://arxiv.org/abs/2405.01553</link>

<description>arXiv:2405.01553v1 Announce Type: cross 

Abstract: Recently, Large Langauge Models (LLMs) have gained a lot of attention in the Software Engineering (SE) community. LLMs or their variants pre-trained on code are used for many SE tasks. A main approach for adapting LLMs to the downstream task is to fine-tune the models. However, with having billions-parameters-LLMs, fine-tuning the models is not practical. An alternative approach is using Parameter Efficient Fine Tuning (PEFT), in which the model parameters are frozen and only a few added parameters are trained. Though the LLMs are used for programming languages such as Python and Java widely, their capability for low-resource languages is limited. In this work, we empirically study PEFT methods, LoRA and Compacter, on CodeT5 and CodeLlama. We will assess their performance compared to fully fine-tuned models, whether they can be used for knowledge transfer from natural language models to code (using T5 and Llama models), and their ability to adapt the learned knowledge to an unseen language. For the unseen language, we aim to study R, as it has a wide community. The adaptability with less computational costs makes LLMs accessible in scenarios where heavy computational resources are not available. Moreover, studying R opens new opportunities for using LLMs for other languages. We anticipate our findings to showcase the capabilities of PEFT for code LLMs for R and reveal the improvement areas.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01553v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Amirreza Esmaeili, Iman Saberi, Fatemeh H. Fard</dc:creator>

</item>

<item>

<title>Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series</title>

<link>https://arxiv.org/abs/2405.01554</link>

<description>arXiv:2405.01554v1 Announce Type: cross 

Abstract: Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01554v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<category>q-bio.NC</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Junggu Choi, Tak Hur, Daniel K. Park, Na-Young Shin, Seung-Koo Lee, Hakbae Lee, Sanghoon Han</dc:creator>

</item>

<item>

<title>Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model</title>

<link>https://arxiv.org/abs/2405.01555</link>

<description>arXiv:2405.01555v1 Announce Type: cross 

Abstract: To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.</description>

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<category>cs.NI</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Xin Tang, Qian Chen, Rong Yu, Xiaohuan Li</dc:creator>

</item>

<item>

<title>Semantically Aligned Question and Code Generation for Automated Insight Generation</title>

<link>https://arxiv.org/abs/2405.01556</link>

<description>arXiv:2405.01556v1 Announce Type: cross 

Abstract: Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can generate code that does not correctly correspond (or align) to the insight. In this paper, we leverage the semantic knowledge of large language models to generate targeted and insightful questions about data and the corresponding code to answer those questions. Then through an empirical study on data from Open-WikiTable, we show that embeddings can be effectively used for filtering out semantically unaligned pairs of question and code. Additionally, we found that generating questions and code together yields more diverse questions.</description>

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<category>cs.SE</category>

<category>cs.AI</category>

<category>cs.CL</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Ananya Singha, Bhavya Chopra, Anirudh Khatry, Sumit Gulwani, Austin Z. Henley, Vu Le, Chris Parnin, Mukul Singh, Gust Verbruggen</dc:creator>

</item>

<item>

<title>Rapid Mobile App Development for Generative AI Agents on MIT App Inventor</title>

<link>https://arxiv.org/abs/2405.01561</link>

<description>arXiv:2405.01561v1 Announce Type: cross 

Abstract: The evolution of Artificial Intelligence (AI) stands as a pivotal force shaping our society, finding applications across diverse domains such as education, sustainability, and safety. Leveraging AI within mobile applications makes it easily accessible to the public, catalyzing its transformative potential. In this paper, we present a methodology for the rapid development of AI agent applications using the development platform provided by MIT App Inventor. To demonstrate its efficacy, we share the development journey of three distinct mobile applications: SynchroNet for fostering sustainable communities; ProductiviTeams for addressing procrastination; and iHELP for enhancing community safety. All three applications seamlessly integrate a spectrum of generative AI features, leveraging OpenAI APIs. Furthermore, we offer insights gleaned from overcoming challenges in integrating diverse tools and AI functionalities, aiming to inspire young developers to join our efforts in building practical AI agent applications.</description>

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<category>cs.SE</category>

<category>cs.AI</category>

<category>cs.CY</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<arxiv:DOI>10.5281/zenodo.10899798</arxiv:DOI>

<arxiv:journal_reference>Journal of advances in information science and technology 2(3) 1-8, March 2024</arxiv:journal_reference>

<dc:creator>Jaida Gao, Calab Su, Etai Miller, Kevin Lu, Yu Meng</dc:creator>

</item>

<item>

<title>Mitigating LLM Hallucinations via Conformal Abstention</title>

<link>https://arxiv.org/abs/2405.01563</link>

<description>arXiv:2405.01563v1 Announce Type: cross 

Abstract: We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of resorting to possibly "hallucinating" a non-sensical or incorrect answer. Building on earlier approaches that use self-consistency as a more reliable measure of model confidence, we propose using the LLM itself to self-evaluate the similarity between each of its sampled responses for a given query. We then further leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate). Experimentally, our resulting conformal abstention method reliably bounds the hallucination rate on various closed-book, open-domain generative question answering datasets, while also maintaining a significantly less conservative abstention rate on a dataset with long responses (Temporal Sequences) compared to baselines using log-probability scores to quantify uncertainty, while achieveing comparable performance on a dataset with short answers (TriviaQA). To evaluate the experiments automatically, one needs to determine if two responses are equivalent given a question. Following standard practice, we use a thresholded similarity function to determine if two responses match, but also provide a method for calibrating the threshold based on conformal prediction, with theoretical guarantees on the accuracy of the match prediction, which might be of independent interest.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01563v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<category>cs.CL</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz, Andr\'as Gy\"orgy, Adam Fisch, Arnaud Doucet, Iuliya Beloshapka, Wei-Hung Weng, Yao-Yuan Yang, Csaba Szepesv\'ari, Ali Taylan Cemgil, Nenad Tomasev</dc:creator>

</item>

<item>

<title>CodeFort: Robust Training for Code Generation Models</title>

<link>https://arxiv.org/abs/2405.01567</link>

<description>arXiv:2405.01567v1 Announce Type: cross 

Abstract: Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade the performance of these models. Improving the robustness of code generation models is crucial to better user experience when these models are deployed in real-world applications. However, existing efforts have not addressed this issue for code generation models. To fill this gap, we propose CodeFort, a framework to improve the robustness of code generation models, generalizing a large variety of code perturbations to enrich the training data and enabling various robust training strategies, mixing data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, all carefully designed to support high-throughput training. Extensive evaluations show that we improve the average robust pass rates of baseline CodeGen models from 14.79 to 21.74. Notably, the improvement in robustness against code-syntax perturbations is evidenced by a significant decrease in pass rate drop from 95.04% to 53.35%</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01567v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras</dc:creator>

</item>

<item>

<title>A Semi-Formal Verification Methodology for Efficient Configuration Coverage of Highly Configurable Digital Designs</title>

<link>https://arxiv.org/abs/2405.01572</link>

<description>arXiv:2405.01572v1 Announce Type: cross 

Abstract: Nowadays, a majority of System-on-Chips (SoCs) make use of Intellectual Property (IP) in order to shorten development cycles. When such IPs are developed, one of the main focuses lies in the high configurability of the design. This flexibility on the design side introduces the challenge of covering a huge state space of IP configurations on the verification side to ensure the functional correctness under every possible parameter setting. The vast number of possibilities does not allow a brute-force approach, and therefore, only a selected number of settings based on typical and extreme assumptions are usually verified. Especially in automotive applications, which need to follow the ISO 26262 functional safety standard, the requirement of covering all significant variants needs to be fulfilled in any case. State-of-the-Art existing verification techniques such as simulation-based verification and formal verification have challenges such as time-space explosion and state-space explosion respectively and therefore, lack behind in verifying highly configurable digital designs efficiently. This paper is focused on a semi-formal verification methodology for efficient configuration coverage of highly configurable digital designs. The methodology focuses on reduced runtime based on simulative and formal methods that allow high configuration coverage. The paper also presents the results when the developed methodology was applied on a highly configurable microprocessor IP and discusses the gained benefits.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01572v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<category>cs.AR</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Aman Kumar, Sebastian Simon</dc:creator>

</item>

<item>

<title>Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository</title>

<link>https://arxiv.org/abs/2405.01573</link>

<description>arXiv:2405.01573v1 Announce Type: cross 

Abstract: LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level in various benchmarks. However, the complexities associated with creating code artifacts like classes, particularly within the context of real-world software repositories, remain underexplored. Existing research often treats class-level generation as an isolated task, neglecting the intricate dependencies and interactions that characterize real-world software development environments. To address this gap, we introduce RepoClassBench, a benchmark designed to rigorously evaluate LLMs in generating complex, class-level code within real-world repositories. RepoClassBench includes natural language to class generation tasks across Java and Python, from a selection of public repositories. We ensure that each class in our dataset not only has cross-file dependencies within the repository but also includes corresponding test cases to verify its functionality. We find that current models struggle with the realistic challenges posed by our benchmark, primarily due to their limited exposure to relevant repository contexts. To address this shortcoming, we introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate &amp; reason about repository-level context in an agent-based framework. Our experiments demonstrate that RRR significantly outperforms existing baselines on RepoClassBench, showcasing its effectiveness across programming languages and in various settings. Our findings emphasize the need for benchmarks that incorporate repository-level dependencies to more accurately reflect the complexities of software development. Our work illustrates the benefits of leveraging specialized tools to enhance LLMs understanding of repository context. We plan to make our dataset and evaluation harness public.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01573v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Ajinkya Deshpande, Anmol Agarwal, Shashank Shet, Arun Iyer, Aditya Kanade, Ramakrishna Bairi, Suresh Parthasarathy</dc:creator>

</item>

<item>

<title>On Using Agent-based Modeling and Simulation for Studying Blockchain Systems</title>

<link>https://arxiv.org/abs/2405.01574</link>

<description>arXiv:2405.01574v1 Announce Type: cross 

Abstract: There is a need for a simulation framework, which is develop as a software using modern engineering approaches (e.g., modularity --i.e., model reuse--, testing, continuous development and continuous integration, automated management of builds, dependencies and documentation) and agile principles, (1) to make rapid prototyping of industrial cases and (2) to carry out their feasibility analysis in a realistic manner (i.e., to test hypothesis by simulating complex experiments involving large numbers of participants of different types acting in one or several blockchain systems).</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01574v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>\"Onder G\"urcan</dc:creator>

</item>

<item>

<title>Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024</title>

<link>https://arxiv.org/abs/2405.01575</link>

<description>arXiv:2405.01575v1 Announce Type: cross 

Abstract: This paper describes our systems for the sub-task I in the Software Mention Detection in Scholarly Publications shared-task. We propose three approaches leveraging different pre-trained language models (BERT, SciBERT, and XLM-R) to tackle this challenge. Our bestperforming system addresses the named entity recognition (NER) problem through a three-stage framework. (1) Entity Sentence Classification - classifies sentences containing potential software mentions; (2) Entity Extraction - detects mentions within classified sentences; (3) Entity Type Classification - categorizes detected mentions into specific software types. Experiments on the official dataset demonstrate that our three-stage framework achieves competitive performance, surpassing both other participating teams and our alternative approaches. As a result, our framework based on the XLM-R-based model achieves a weighted F1-score of 67.80%, delivering our team the 3rd rank in Sub-task I for the Software Mention Recognition task.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01575v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<category>cs.CL</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Thuy Nguyen Thi, Anh Nguyen Viet, Thin Dang Van, Ngan Nguyen Luu Thuy</dc:creator>

</item>

<item>

<title>Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant</title>

<link>https://arxiv.org/abs/2405.01576</link>

<description>arXiv:2405.01576v1 Announce Type: cross 

Abstract: We study the tendency of AI systems to deceive by constructing a realistic simulation setting of a company AI assistant. The simulated company employees provide tasks for the assistant to complete, these tasks spanning writing assistance, information retrieval and programming. We then introduce situations where the model might be inclined to behave deceptively, while taking care to not instruct or otherwise pressure the model to do so. Across different scenarios, we find that Claude 3 Opus

1) complies with a task of mass-generating comments to influence public perception of the company, later deceiving humans about it having done so,

2) lies to auditors when asked questions, and

3) strategically pretends to be less capable than it is during capability evaluations.

Our work demonstrates that even models trained to be helpful, harmless and honest sometimes behave deceptively in realistic scenarios, without notable external pressure to do so.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01576v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Olli J\"arviniemi, Evan Hubinger</dc:creator>

</item>

<item>

<title>On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation</title>

<link>https://arxiv.org/abs/2405.01580</link>

<description>arXiv:2405.01580v1 Announce Type: cross 

Abstract: The task of code generation from natural language (NL2Code) has become extremely popular, especially with the advent of Large Language Models (LLMs). However, efforts to quantify and track this progress have suffered due to a lack of reliable metrics for functional correctness. While popular benchmarks like HumanEval have test cases to enable reliable evaluation of correctness, it is time-consuming and requires human effort to collect test cases. As an alternative several reference-based evaluation metrics have been proposed, with embedding-based metrics like CodeBERTScore being touted as having a high correlation with human preferences and functional correctness. In our work, we analyze the ability of embedding-based metrics like CodeBERTScore to measure functional correctness and other helpful constructs like editing effort by analyzing outputs of ten models over two popular code generation benchmarks. Our results show that while they have a weak correlation with functional correctness (0.16), they are strongly correlated (0.72) with editing effort.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01580v1</guid>

<category>cs.SE</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Atharva Naik</dc:creator>

</item>

<item>

<title>The Mercurial Top-Level Ontology of Large Language Models</title>

<link>https://arxiv.org/abs/2405.01581</link>

<description>arXiv:2405.01581v1 Announce Type: cross 

Abstract: In our work, we systematize and analyze implicit ontological commitments in the responses generated by large language models (LLMs), focusing on ChatGPT 3.5 as a case study. We investigate how LLMs, despite having no explicit ontology, exhibit implicit ontological categorizations that are reflected in the texts they generate. The paper proposes an approach to understanding the ontological commitments of LLMs by defining ontology as a theory that provides a systematic account of the ontological commitments of some text. We investigate the ontological assumptions of ChatGPT and present a systematized account, i.e., GPT's top-level ontology. This includes a taxonomy, which is available as an OWL file, as well as a discussion about ontological assumptions (e.g., about its mereology or presentism). We show that in some aspects GPT's top-level ontology is quite similar to existing top-level ontologies. However, there are significant challenges arising from the flexible nature of LLM-generated texts, including ontological overload, ambiguity, and inconsistency.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01581v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Nele K\"ohler, Fabian Neuhaus</dc:creator>

</item>

<item>

<title>Text Quality-Based Pruning for Efficient Training of Language Models</title>

<link>https://arxiv.org/abs/2405.01582</link>

<description>arXiv:2405.01582v1 Announce Type: cross 

Abstract: In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this paper we propose a novel method for numerically evaluating text quality in large unlabelled NLP datasets in a model agnostic manner to assign the text instances a "quality score".

By proposing the text quality metric, the paper establishes a framework to identify and eliminate low-quality text instances, leading to improved training efficiency for LM models. Experimental results over multiple models and datasets demonstrate the efficacy of this approach, showcasing substantial gains in training effectiveness and highlighting the potential for resource-efficient LM training.

For example, we observe an absolute accuracy improvement of 0.9% averaged over 14 downstream evaluation tasks for multiple LM models while using 40% lesser data and training 42% faster when training on the OpenWebText dataset and 0.8% average absolute accuracy improvement while using 20% lesser data and training 21% faster on the Wikipedia dataset.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01582v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Vasu Sharma, Karthik Padthe, Newsha Ardalani, Kushal Tirumala, Russell Howes, Hu Xu, Po-Yao Huang, Shang-Wen Li, Armen Aghajanyan, Gargi Ghosh</dc:creator>

</item>

<item>

<title>MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning</title>

<link>https://arxiv.org/abs/2405.01583</link>

<description>arXiv:2405.01583v1 Announce Type: cross 

Abstract: The MEDIQA-M3G 2024 challenge necessitates novel solutions for Multilingual &amp; Multimodal Medical Answer Generation in dermatology (wai Yim et al., 2024a). This paper addresses the limitations of traditional methods by proposing a weakly supervised learning approach for open-ended medical question-answering (QA). Our system leverages readily available MEDIQA-M3G images via a VGG16-CNN-SVM model, enabling multilingual (English, Chinese, Spanish) learning of informative skin condition representations. Using pre-trained QA models, we further bridge the gap between visual and textual information through multimodal fusion. This approach tackles complex, open-ended questions even without predefined answer choices. We empower the generation of comprehensive answers by feeding the ViT-CLIP model with multiple responses alongside images. This work advances medical QA research, paving the way for clinical decision support systems and ultimately improving healthcare delivery.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01583v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.CV</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<dc:creator>Nadia Saeed</dc:creator>

</item>

<item>

<title>Improve Academic Query Resolution through BERT-based Question Extraction from Images</title>

<link>https://arxiv.org/abs/2405.01587</link>

<description>arXiv:2405.01587v1 Announce Type: cross 

Abstract: Providing fast and accurate resolution to the student's query is an essential solution provided by Edtech organizations. This is generally provided with a chat-bot like interface to enable students to ask their doubts easily. One preferred format for student queries is images, as it allows students to capture and post questions without typing complex equations and information. However, this format also presents difficulties, as images may contain multiple questions or textual noise that lowers the accuracy of existing single-query answering solutions. In this paper, we propose a method for extracting questions from text or images using a BERT-based deep learning model and compare it to the other rule-based and layout-based methods. Our method aims to improve the accuracy and efficiency of student query resolution in Edtech organizations.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01587v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.CV</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<arxiv:DOI>10.1109/IATMSI60426.2024.10502904</arxiv:DOI>

<arxiv:journal_reference>2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) volume 2 (2024) 1-4</arxiv:journal_reference>

<dc:creator>Nidhi Kamal, Saurabh Yadav, Jorawar Singh, Aditi Avasthi</dc:creator>

</item>

<item>

<title>Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL</title>

<link>https://arxiv.org/abs/2405.01588</link>

<description>arXiv:2405.01588v1 Announce Type: cross 

Abstract: Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses. The EHRSQL dataset stands out as a promising benchmark because it is the only dataset that incorporates unanswerable questions in the EHR QA system alongside practical questions. However, in this work, we identify a data bias in these unanswerable questions; they can often be discerned simply by filtering with specific N-gram patterns. Such biases jeopardize the authenticity and reliability of QA system evaluations. To tackle this problem, we propose a simple debiasing method of adjusting the split between the validation and test sets to neutralize the undue influence of N-gram filtering. By experimenting on the MIMIC-III dataset, we demonstrate both the existing data bias in EHRSQL and the effectiveness of our data split strategy in mitigating this bias.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01588v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Yongjin Yang, Sihyeon Kim, SangMook Kim, Gyubok Lee, Se-Young Yun, Edward Choi</dc:creator>

</item>

<item>

<title>GPT-4 passes most of the 297 written Polish Board Certification Examinations</title>

<link>https://arxiv.org/abs/2405.01589</link>

<description>arXiv:2405.01589v1 Announce Type: cross 

Abstract: Introduction: Recently, the effectiveness of Large Language Models (LLMs) has increased rapidly, allowing them to be used in a great number of applications. However, the risks posed by the generation of false information through LLMs significantly limit their applications in sensitive areas such as healthcare, highlighting the necessity for rigorous validations to determine their utility and reliability. To date, no study has extensively compared the performance of LLMs on Polish medical examinations across a broad spectrum of specialties on a very large dataset. Objectives: This study evaluated the performance of three Generative Pretrained Transformer (GPT) models on the Polish Board Certification Exam (Pa\'nstwowy Egzamin Specjalizacyjny, PES) dataset, which consists of 297 tests. Methods: We developed a software program to download and process PES exams and tested the performance of GPT models using OpenAI Application Programming Interface. Results: Our findings reveal that GPT-3.5 did not pass any of the analyzed exams. In contrast, the GPT-4 models demonstrated the capability to pass the majority of the exams evaluated, with the most recent model, gpt-4-0125, successfully passing 222 (75%) of them. The performance of the GPT models varied significantly, displaying excellence in exams related to certain specialties while completely failing others. Conclusions: The significant progress and impressive performance of LLM models hold great promise for the increased application of AI in the field of medicine in Poland. For instance, this advancement could lead to the development of AI-based medical assistants for healthcare professionals, enhancing the efficiency and accuracy of medical services.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01589v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Jakub Pokrywka, Jeremi Kaczmarek, Edward Gorzela\'nczyk</dc:creator>

</item>

<item>

<title>Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model</title>

<link>https://arxiv.org/abs/2405.01591</link>

<description>arXiv:2405.01591v1 Announce Type: cross 

Abstract: Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data for effective in-context learning raises questions about the feasibility of these models when encountering with the inevitable variations and errors inherent in real-world medical data. In this paper, we introduce MID-M, a novel framework that leverages the in-context learning capabilities of a general-domain Large Language Model (LLM) to process multimodal data via image descriptions. MID-M achieves a comparable or superior performance to task-specific fine-tuned LMMs and other general-domain ones, without the extensive domain-specific training or pre-training on multimodal data, with significantly fewer parameters. This highlights the potential of leveraging general-domain LLMs for domain-specific tasks and offers a sustainable and cost-effective alternative to traditional LMM developments. Moreover, the robustness of MID-M against data quality issues demonstrates its practical utility in real-world medical domain applications.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01591v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>eess.IV</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Seonhee Cho, Choonghan Kim, Jiho Lee, Chetan Chilkunda, Sujin Choi, Joo Heung Yoon</dc:creator>

</item>

<item>

<title>Text and Audio Simplification: Human vs. ChatGPT</title>

<link>https://arxiv.org/abs/2405.01592</link>

<description>arXiv:2405.01592v1 Announce Type: cross 

Abstract: Text and audio simplification to increase information comprehension are important in healthcare. With the introduction of ChatGPT, an evaluation of its simplification performance is needed. We provide a systematic comparison of human and ChatGPT simplified texts using fourteen metrics indicative of text difficulty. We briefly introduce our online editor where these simplification tools, including ChatGPT, are available. We scored twelve corpora using our metrics: six text, one audio, and five ChatGPT simplified corpora. We then compare these corpora with texts simplified and verified in a prior user study. Finally, a medical domain expert evaluated these texts and five, new ChatGPT simplified versions. We found that simple corpora show higher similarity with the human simplified texts. ChatGPT simplification moves metrics in the right direction. The medical domain expert evaluation showed a preference for the ChatGPT style, but the text itself was rated lower for content retention.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01592v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Gondy Leroy, David Kauchak, Philip Harber, Ankit Pal, Akash Shukla</dc:creator>

</item>

<item>

<title>Large Language Model Agent for Fake News Detection</title>

<link>https://arxiv.org/abs/2405.01593</link>

<description>arXiv:2405.01593v1 Announce Type: cross 

Abstract: In the current digital era, the rapid spread of misinformation on online platforms presents significant challenges to societal well-being, public trust, and democratic processes, influencing critical decision making and public opinion. To address these challenges, there is a growing need for automated fake news detection mechanisms. Pre-trained large language models (LLMs) have demonstrated exceptional capabilities across various natural language processing (NLP) tasks, prompting exploration into their potential for verifying news claims. Instead of employing LLMs in a non-agentic way, where LLMs generate responses based on direct prompts in a single shot, our work introduces FactAgent, an agentic approach of utilizing LLMs for fake news detection. FactAgent enables LLMs to emulate human expert behavior in verifying news claims without any model training, following a structured workflow. This workflow breaks down the complex task of news veracity checking into multiple sub-steps, where LLMs complete simple tasks using their internal knowledge or external tools. At the final step of the workflow, LLMs integrate all findings throughout the workflow to determine the news claim's veracity. Compared to manual human verification, FactAgent offers enhanced efficiency. Experimental studies demonstrate the effectiveness of FactAgent in verifying claims without the need for any training process. Moreover, FactAgent provides transparent explanations at each step of the workflow and during final decision-making, offering insights into the reasoning process of fake news detection for end users. FactAgent is highly adaptable, allowing for straightforward updates to its tools that LLMs can leverage within the workflow, as well as updates to the workflow itself using domain knowledge. This adaptability enables FactAgent's application to news verification across various domains.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01593v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.IR</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Xinyi Li, Yongfeng Zhang, Edward C. Malthouse</dc:creator>

</item>

<item>

<title>Unifying and extending Precision Recall metrics for assessing generative models</title>

<link>https://arxiv.org/abs/2405.01611</link>

<description>arXiv:2405.01611v1 Announce Type: cross 

Abstract: With the recent success of generative models in image and text, the evaluation of generative models has gained a lot of attention. Whereas most generative models are compared in terms of scalar values such as Frechet Inception Distance (FID) or Inception Score (IS), in the last years (Sajjadi et al., 2018) proposed a definition of precision-recall curve to characterize the closeness of two distributions. Since then, various approaches to precision and recall have seen the light (Kynkaanniemi et al., 2019; Naeem et al., 2020; Park &amp; Kim, 2023). They center their attention on the extreme values of precision and recall, but apart from this fact, their ties are elusive. In this paper, we unify most of these approaches under the same umbrella, relying on the work of (Simon et al., 2019). Doing so, we were able not only to recover entire curves, but also to expose the sources of the accounted pitfalls of the concerned metrics. We also provide consistency results that go well beyond the ones presented in the corresponding literature. Last, we study the different behaviors of the curves obtained experimentally.</description>

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<category>cs.LG</category>

<category>cs.AI</category>

<category>stat.ME</category>

<category>stat.ML</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Benjamin Sykes, Loic Simon, Julien Rabin</dc:creator>

</item>

<item>

<title>A probabilistic estimation of remaining useful life from censored time-to-event data</title>

<link>https://arxiv.org/abs/2405.01614</link>

<description>arXiv:2405.01614v1 Announce Type: cross 

Abstract: Predicting the remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. A common definition of the RUL is the time until a bearing is no longer functional, which we denote as an event, and many data-driven methods have been proposed to predict the RUL. However, few studies have addressed the problem of censored data, where this event of interest is not observed, and simply ignoring these observations can lead to an overestimation of the failure risk. In this paper, we propose a probabilistic estimation of RUL using survival analysis that supports censored data. First, we analyze sensor readings from ball bearings in the frequency domain and annotate when a bearing starts to deteriorate by calculating the Kullback-Leibler (KL) divergence between the probability density function (PDF) of the current process and a reference PDF. Second, we train several survival models on the annotated bearing dataset, capable of predicting the RUL over a finite time horizon using the survival function. This function is guaranteed to be strictly monotonically decreasing and is an intuitive estimation of the remaining lifetime. We demonstrate our approach in the XJTU-SY dataset using cross-validation and find that Random Survival Forests consistently outperforms both non-neural networks and neural networks in terms of the mean absolute error (MAE). Our work encourages the inclusion of censored data in predictive maintenance models and highlights the unique advantages that survival analysis offers when it comes to probabilistic RUL estimation and early fault detection.</description>

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<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen, Manuel Morante, Christian Fischer Pedersen</dc:creator>

</item>

<item>

<title>Generative Active Learning for the Search of Small-molecule Protein Binders</title>

<link>https://arxiv.org/abs/2405.01616</link>

<description>arXiv:2405.01616v1 Announce Type: cross 

Abstract: Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.</description>

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<category>q-bio.BM</category>

<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>

<dc:creator>Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Ramp\'a\v{s}ek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, Jos\'e Miguel Hern\'andez-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio</dc:creator>

</item>

<item>

<title>Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts</title>

<link>https://arxiv.org/abs/2405.01660</link>

<description>arXiv:2405.01660v1 Announce Type: cross 

Abstract: Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans versus fine-tuned RoBERTa classifiers to detect AI-generated texts. We conclude that human evaluators rate the generated texts slightly worse on average regarding their creative quality, but they are unable to reliably distinguish between human-written and AI-generated texts. We further provide a dataset for creative, witty text generation based on Reddit Showerthoughts posts.</description>

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<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Tolga Buz, Benjamin Frost, Nikola Genchev, Moritz Schneider, Lucie-Aim\'ee Kaffee, Gerard de Melo</dc:creator>

</item>

<item>

<title>ATNPA: A Unified View of Oversmoothing Alleviation in Graph Neural Networks</title>

<link>https://arxiv.org/abs/2405.01663</link>

<description>arXiv:2405.01663v1 Announce Type: cross 

Abstract: Oversmoothing is a commonly observed challenge in graph neural network (GNN) learning, where, as layers increase, embedding features learned from GNNs quickly become similar/indistinguishable, making them incapable of differentiating network proximity. A GNN with shallow layer architectures can only learn short-term relation or localized structure information, limiting its power of learning long-term connection, evidenced by their inferior learning performance on heterophilous graphs. Tackling oversmoothing is crucial to harness deep-layer architectures for GNNs. To date, many methods have been proposed to alleviate oversmoothing. The vast difference behind their design principles, combined with graph complications, make it difficult to understand and even compare their difference in tackling the oversmoothing. In this paper, we propose ATNPA, a unified view with five key steps: Augmentation, Transformation, Normalization, Propagation, and Aggregation, to summarize GNN oversmoothing alleviation approaches. We first outline three themes to tackle oversmoothing, and then separate all methods into six categories, followed by detailed reviews of representative methods, including their relation to the ATNPA, and discussion about their niche, strength, and weakness. The review not only draws in-depth understanding of existing methods in the field, but also shows a clear road map for future study.</description>

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<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>

<dc:creator>Yufei Jin, Xingquan Zhu</dc:creator>

</item>

<item>

<title>Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation</title>

<link>https://arxiv.org/abs/2405.01677</link>

<description>arXiv:2405.01677v1 Announce Type: cross 

Abstract: Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performance through policy adjustments may adversely affect safety performance. In this study, we aim to address this conflicting relation by leveraging the theory of gradient manipulation. Initially, we analyze the conflict between reward and safety gradients. Subsequently, we tackle the balance between reward and safety optimization by proposing a soft switching policy optimization method, for which we provide convergence analysis. Based on our theoretical examination, we provide a safe RL framework to overcome the aforementioned challenge, and we develop a Safety-MuJoCo Benchmark to assess the performance of safe RL algorithms. Finally, we evaluate the effectiveness of our method on the Safety-MuJoCo Benchmark and a popular safe benchmark, Omnisafe. Experimental results demonstrate that our algorithms outperform several state-of-the-art baselines in terms of balancing reward and safety optimization.</description>

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<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, Qingwei Lin, Ming Jin, Alois Knoll</dc:creator>

</item>

<item>

<title>Leveraging Prompt-Learning for Structured Information Extraction from Crohn's Disease Radiology Reports in a Low-Resource Language</title>

<link>https://arxiv.org/abs/2405.01682</link>

<description>arXiv:2405.01682v1 Announce Type: cross 

Abstract: Automatic conversion of free-text radiology reports into structured data using Natural Language Processing (NLP) techniques is crucial for analyzing diseases on a large scale. While effective for tasks in widely spoken languages like English, generative large language models (LLMs) typically underperform with less common languages and can pose potential risks to patient privacy. Fine-tuning local NLP models is hindered by the skewed nature of real-world medical datasets, where rare findings represent a significant data imbalance. We introduce SMP-BERT, a novel prompt learning method that leverages the structured nature of reports to overcome these challenges. In our studies involving a substantial collection of Crohn's disease radiology reports in Hebrew (over 8,000 patients and 10,000 reports), SMP-BERT greatly surpassed traditional fine-tuning methods in performance, notably in detecting infrequent conditions (AUC: 0.99 vs 0.94, F1: 0.84 vs 0.34). SMP-BERT empowers more accurate AI diagnostics available for low-resource languages.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01682v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<dc:creator>Liam Hazan, Gili Focht, Naama Gavrielov, Roi Reichart, Talar Hagopian, Mary-Louise C. Greer, Ruth Cytter Kuint, Dan Turner, Moti Freiman</dc:creator>

</item>

<item>

<title>Intelligent Switching for Reset-Free RL</title>

<link>https://arxiv.org/abs/2405.01684</link>

<description>arXiv:2405.01684v1 Announce Type: cross 

Abstract: In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01684v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Darshan Patil, Janarthanan Rajendran, Glen Berseth, Sarath Chandar</dc:creator>

</item>

<item>

<title>Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models</title>

<link>https://arxiv.org/abs/2405.01686</link>

<description>arXiv:2405.01686v1 Announce Type: cross 

Abstract: Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treatment effectiveness, results from meta-analyses are considered the strongest form of evidence. However, rigorous evidence syntheses are time-consuming and labor-intensive, requiring manual extraction of data from individual trials to be synthesized. Ideally, language technologies would permit fully automatic meta-analysis, on demand. This requires accurately extracting numerical results from individual trials, which has been beyond the capabilities of natural language processing (NLP) models to date. In this work, we evaluate whether modern large language models (LLMs) can reliably perform this task. We annotate (and release) a modest but granular evaluation dataset of clinical trial reports with numerical findings attached to interventions, comparators, and outcomes. Using this dataset, we evaluate the performance of seven LLMs applied zero-shot for the task of conditionally extracting numerical findings from trial reports. We find that massive LLMs that can accommodate lengthy inputs are tantalizingly close to realizing fully automatic meta-analysis, especially for dichotomous (binary) outcomes (e.g., mortality). However, LLMs -- including ones trained on biomedical texts -- perform poorly when the outcome measures are complex and tallying the results requires inference. This work charts a path toward fully automatic meta-analysis of RCTs via LLMs, while also highlighting the limitations of existing models for this aim.</description>

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<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Hye Sun Yun, David Pogrebitskiy, Iain J. Marshall, Byron C. Wallace</dc:creator>

</item>

<item>

<title>SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients</title>

<link>https://arxiv.org/abs/2405.01699</link>

<description>arXiv:2405.01699v1 Announce Type: cross 

Abstract: Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noise. Traditional methods using transformer-based models often face limitations stemming from the lack of specialized databases, which adversely affect their performance with objects of varying orientations and scales. This underscores the need for more adaptable, lightweight models. In response, this paper introduces two innovative approaches that significantly enhance detection and segmentation capabilities for small aerial objects. Firstly, we explore the use of the SAHI framework on the newly introduced lightweight YOLO v9 architecture, which utilizes Programmable Gradient Information (PGI) to reduce the substantial information loss typically encountered in sequential feature extraction processes. The paper employs the Vision Mamba model, which incorporates position embeddings to facilitate precise location-aware visual understanding, combined with a novel bidirectional State Space Model (SSM) for effective visual context modeling. This State Space Model adeptly harnesses the linear complexity of CNNs and the global receptive field of Transformers, making it particularly effective in remote sensing image classification. Our experimental results demonstrate substantial improvements in detection accuracy and processing efficiency, validating the applicability of these approaches for real-time small object detection across diverse aerial scenarios. This paper also discusses how these methodologies could serve as foundational models for future advancements in aerial object recognition technologies. The source code will be made accessible here.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01699v1</guid>

<category>cs.CV</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Tushar Verma, Jyotsna Singh, Yash Bhartari, Rishi Jarwal, Suraj Singh, Shubhkarman Singh</dc:creator>

</item>

<item>

<title>Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning</title>

<link>https://arxiv.org/abs/2405.01705</link>

<description>arXiv:2405.01705v1 Announce Type: cross 

Abstract: Image and multimodal machine learning tasks are very challenging to solve in the case of poorly distributed data. In particular, data availability and privacy restrictions exacerbate these hurdles in the medical domain. The state of the art in image generation quality is held by Latent Diffusion models, making them prime candidates for tackling this problem. However, a few key issues still need to be solved, such as the difficulty in generating data from under-represented classes and a slow inference process. To mitigate these issues, we propose a new method for image augmentation in long-tailed data based on leveraging the rich latent space of pre-trained Stable Diffusion Models. We create a modified separable latent space to mix head and tail class examples. We build this space via Iterated Learning of underlying sparsified embeddings, which we apply to task-specific saliency maps via a K-NN approach. Code is available at https://github.com/SugarFreeManatee/Feature-Space-Augmentation-and-Iterated-Learning</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01705v1</guid>

<category>cs.CV</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Rafael Elberg, Denis Parra, Mircea Petrache</dc:creator>

</item>

<item>

<title>Individual Fairness Through Reweighting and Tuning</title>

<link>https://arxiv.org/abs/2405.01711</link>

<description>arXiv:2405.01711v1 Announce Type: cross 

Abstract: Inherent bias within society can be amplified and perpetuated by artificial intelligence (AI) systems. To address this issue, a wide range of solutions have been proposed to identify and mitigate bias and enforce fairness for individuals and groups. Recently, Graph Laplacian Regularizer (GLR), a regularization technique from the semi-supervised learning literature has been used as a substitute for the common Lipschitz condition to enhance individual fairness (IF). Notable prior work has shown that enforcing IF through a GLR can improve the transfer learning accuracy of AI models under covariate shifts. However, the prior work defines a GLR on the source and target data combined, implicitly assuming that the target data are available at train time, which might not hold in practice. In this work, we investigated whether defining a GLR independently on the train and target data could maintain similar accuracy compared to the prior work model. Furthermore, we introduced the Normalized Fairness Gain score (FGN) to measure IF for in-processing algorithmic fairness techniques. FGN quantifies the amount of gained fairness when a GLR is used versus not. We evaluated the new and original methods under FGN, the Prediction Consistency (PC), and traditional classification metrics on the German Credit Approval dataset. The results showed that the two models achieved similar statistical mean performances over five-fold cross-validation. Furthermore, the proposed metric showed that PC scores can be misleading as the scores can be high and statistically similar to fairness-enhanced models while FGN scores are small. This work therefore provides new insights into when a GLR effectively enhances IF and the pitfalls of PC.</description>

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<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Abdoul Jalil Djiberou Mahamadou, Lea Goetz, Russ Altman</dc:creator>

</item>

<item>

<title>Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps</title>

<link>https://arxiv.org/abs/2405.01714</link>

<description>arXiv:2405.01714v1 Announce Type: cross 

Abstract: Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have been advanced for early sepsis prediction, their 'black-box' nature obscures the internal logic, impairing interpretability in critical settings like ICUs. This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making. We show that the attention mechanism could be adapted to various black box time series forecasting models such as N-HiTS and N-BEATS. Our method preserves the accuracy of conventional deep learning models while enhancing interpretability through attention-weight-generated heatmaps. We evaluated our model on the eICU-CRD dataset, focusing on forecasting vital signs for sepsis patients. We assessed its performance using mean squared error (MSE) and dynamic time warping (DTW) metrics. We explored the attention maps of N-HiTS and N-BEATS, examining the differences in their performance and identifying crucial factors influencing vital sign forecasting.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01714v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>

<dc:creator>Yuwei Liu, Chen Dan, Anubhav Bhatti, Bingjie Shen, Divij Gupta, Suraj Parmar, San Lee</dc:creator>

</item>

<item>

<title>Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion</title>

<link>https://arxiv.org/abs/2405.01723</link>

<description>arXiv:2405.01723v1 Announce Type: cross 

Abstract: Detecting and segmenting moving objects from a moving monocular camera is challenging in the presence of unknown camera motion, diverse object motions and complex scene structures. Most existing methods rely on a single motion cue to perform motion segmentation, which is usually insufficient when facing different complex environments. While a few recent deep learning based methods are able to combine multiple motion cues to achieve improved accuracy, they depend heavily on vast datasets and extensive annotations, making them less adaptable to new scenarios. To address these limitations, we propose a novel monocular dense segmentation method that achieves state-of-the-art motion segmentation results in a zero-shot manner. The proposed method synergestically combines the strengths of deep learning and geometric model fusion methods by performing geometric model fusion on object proposals. Experiments show that our method achieves competitive results on several motion segmentation datasets and even surpasses some state-of-the-art supervised methods on certain benchmarks, while not being trained on any data. We also present an ablation study to show the effectiveness of combining different geometric models together for motion segmentation, highlighting the value of our geometric model fusion strategy.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01723v1</guid>

<category>cs.CV</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Yuxiang Huang, Yuhao Chen, John Zelek</dc:creator>

</item>

<item>

<title>Large Language Models are Inconsistent and Biased Evaluators</title>

<link>https://arxiv.org/abs/2405.01724</link>

<description>arXiv:2405.01724v1 Announce Type: cross 

Abstract: The zero-shot capability of Large Language Models (LLMs) has enabled highly flexible, reference-free metrics for various tasks, making LLM evaluators common tools in NLP. However, the robustness of these LLM evaluators remains relatively understudied; existing work mainly pursued optimal performance in terms of correlating LLM scores with human expert scores. In this paper, we conduct a series of analyses using the SummEval dataset and confirm that LLMs are biased evaluators as they: (1) exhibit familiarity bias-a preference for text with lower perplexity, (2) show skewed and biased distributions of ratings, and (3) experience anchoring effects for multi-attribute judgments. We also found that LLMs are inconsistent evaluators, showing low "inter-sample" agreement and sensitivity to prompt differences that are insignificant to human understanding of text quality. Furthermore, we share recipes for configuring LLM evaluators to mitigate these limitations. Experimental results on the RoSE dataset demonstrate improvements over the state-of-the-art LLM evaluators.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01724v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara</dc:creator>

</item>

<item>

<title>Diabetic Retinopathy Detection Using Quantum Transfer Learning</title>

<link>https://arxiv.org/abs/2405.01734</link>

<description>arXiv:2405.01734v1 Announce Type: cross 

Abstract: Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The traditional process of diagnosing DR through retina fundus images by ophthalmologists is not only time-intensive but also expensive. While classical transfer learning models have been widely adopted for computer-aided detection of DR, their high maintenance costs can hinder their detection efficiency. In contrast, Quantum Transfer Learning offers a more effective solution to this challenge. This approach is notably advantageous because it operates on heuristic principles, making it highly optimized for the task. Our proposed methodology leverages this hybrid quantum transfer learning technique to detect DR. To construct our model, we utilize the APTOS 2019 Blindness Detection dataset, available on Kaggle. We employ the ResNet-18, ResNet34, ResNet50, ResNet101, ResNet152 and Inception V3, pre-trained classical neural networks, for the initial feature extraction. For the classification stage, we use a Variational Quantum Classifier. Our hybrid quantum model has shown remarkable results, achieving an accuracy of 97% for ResNet-18. This demonstrates that quantum computing, when integrated with quantum machine learning, can perform tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these advanced technologies, we can significantly improve the detection and diagnosis of Diabetic Retinopathy, potentially saving many from the risk of blindness.

Keywords: Diabetic Retinopathy, Quantum Transfer Learning, Deep Learning</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01734v1</guid>

<category>cs.CV</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Ankush Jain, Rinav Gupta, Jai Singhal</dc:creator>

</item>

<item>

<title>PVF (Parameter Vulnerability Factor): A Quantitative Metric Measuring AI Vulnerability and Resilience Against Parameter Corruptions</title>

<link>https://arxiv.org/abs/2405.01741</link>

<description>arXiv:2405.01741v1 Announce Type: cross 

Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them inevitably and increasingly susceptible to hardware faults (e.g., bit flips) that can potentially corrupt model parameters. Given this challenge, this paper aims to answer a critical question: How likely is a parameter corruption to result in an incorrect model output? To systematically answer this question, we propose a novel quantitative metric, Parameter Vulnerability Factor (PVF), inspired by architectural vulnerability factor (AVF) in computer architecture community, aiming to standardize the quantification of AI model resilience/vulnerability against parameter corruptions. We define a model parameter's PVF as the probability that a corruption in that particular model parameter will result in an incorrect output. Similar to AVF, this statistical concept can be derived from statistically extensive and meaningful fault injection (FI) experiments. In this paper, we present several use cases on applying PVF to three types of tasks/models during inference -- recommendation (DLRM), vision classification (CNN), and text classification (BERT). PVF can provide pivotal insights to AI hardware designers in balancing the tradeoff between fault protection and performance/efficiency such as mapping vulnerable AI parameter components to well-protected hardware modules. PVF metric is applicable to any AI model and has a potential to help unify and standardize AI vulnerability/resilience evaluation practice.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01741v1</guid>

<category>cs.CR</category>

<category>cs.AI</category>

<category>cs.AR</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Xun Jiao, Fred Lin, Harish D. Dixit, Joel Coburn, Abhinav Pandey, Han Wang, Jianyu Huang, Venkat Ramesh, Wang Xu, Daniel Moore, Sriram Sankar</dc:creator>

</item>

<item>

<title>ALCM: Autonomous LLM-Augmented Causal Discovery Framework</title>

<link>https://arxiv.org/abs/2405.01744</link>

<description>arXiv:2405.01744v1 Announce Type: cross 

Abstract: To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP-hard problem. Recently, the advent of Large Language Models (LLMs) has ushered in a new era, indicating their emergent capabilities and widespread applicability in facilitating causal reasoning across diverse domains, such as medicine, finance, and science. The expansive knowledge base of LLMs holds the potential to elevate the field of causal reasoning by offering interpretability, making inferences, generalizability, and uncovering novel causal structures. In this paper, we introduce a new framework, named Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize data-driven causal discovery algorithms and LLMs, automating the generation of a more resilient, accurate, and explicable causal graph. The ALCM consists of three integral components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components autonomously collaborate within a dynamic environment to address causal discovery questions and deliver plausible causal graphs. We evaluate the ALCM framework by implementing two demonstrations on seven well-known datasets. Experimental results demonstrate that ALCM outperforms existing LLM methods and conventional data-driven causal reasoning mechanisms. This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01744v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<category>cs.CL</category>

<category>stat.ME</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani</dc:creator>

</item>

<item>

<title>Segmentation-Free Outcome Prediction in Head and Neck Cancer: Deep Learning-based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs) of PET Images</title>

<link>https://arxiv.org/abs/2405.01756</link>

<description>arXiv:2405.01756v1 Announce Type: cross 

Abstract: We introduce an innovative, simple, effective segmentation-free approach for outcome prediction in head \&amp; neck cancer (HNC) patients. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) volumes, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained to perform automatic cropping of the head and neck region on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01756v1</guid>

<category>physics.med-ph</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Amirhosein Toosi, Isaac Shiri, Habib Zaidi, Arman Rahmim</dc:creator>

</item>

<item>

<title>Reinforcement Learning-Guided Semi-Supervised Learning</title>

<link>https://arxiv.org/abs/2405.01760</link>

<description>arXiv:2405.01760v1 Announce Type: cross 

Abstract: In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data. They are limited to exploiting loss functions and regularization methods within the standard norm. In this paper, we propose a novel Reinforcement Learning (RL) Guided SSL method, RLGSSL, that formulates SSL as a one-armed bandit problem and deploys an innovative RL loss based on weighted reward to adaptively guide the learning process of the prediction model. RLGSSL incorporates a carefully designed reward function that balances the use of labeled and unlabeled data to enhance generalization performance. A semi-supervised teacher-student framework is further deployed to increase the learning stability. We demonstrate the effectiveness of RLGSSL through extensive experiments on several benchmark datasets and show that our approach achieves consistent superior performance compared to state-of-the-art SSL methods.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01760v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Marzi Heidari, Hanping Zhang, Yuhong Guo</dc:creator>

</item>

<item>

<title>CoS: Enhancing Personalization and Mitigating Bias with Context Steering</title>

<link>https://arxiv.org/abs/2405.01768</link>

<description>arXiv:2405.01768v1 Announce Type: cross 

Abstract: When querying a large language model (LLM), the context, i.e. personal, demographic, and cultural information specific to an end-user, can significantly shape the response of the LLM. For example, asking the model to explain Newton's second law with the context "I am a toddler" yields a different answer compared to the context "I am a physics professor." Proper usage of the context enables the LLM to generate personalized responses, whereas inappropriate contextual influence can lead to stereotypical and potentially harmful generations (e.g. associating "female" with "housekeeper"). In practice, striking the right balance when leveraging context is a nuanced and challenging problem that is often situation-dependent. One common approach to address this challenge is to fine-tune LLMs on contextually appropriate responses. However, this approach is expensive, time-consuming, and not controllable for end-users in different situations. In this work, we propose Context Steering (CoS) - a simple training-free method that can be easily applied to autoregressive LLMs at inference time. By measuring the contextual influence in terms of token prediction likelihood and modulating it, our method enables practitioners to determine the appropriate level of contextual influence based on their specific use case and end-user base. We showcase a variety of applications of CoS including amplifying the contextual influence to achieve better personalization and mitigating unwanted influence for reducing model bias. In addition, we show that we can combine CoS with Bayesian Inference to quantify the extent of hate speech on the internet. We demonstrate the effectiveness of CoS on state-of-the-art LLMs and benchmarks.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01768v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Jerry Zhi-Yang He, Sashrika Pandey, Mariah L. Schrum, Anca Dragan</dc:creator>

</item>

<item>

<title>An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions</title>

<link>https://arxiv.org/abs/2405.01776</link>

<description>arXiv:2405.01776v1 Announce Type: cross 

Abstract: With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models.

If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&amp;D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic.

We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01776v1</guid>

<category>cs.RO</category>

<category>cs.AI</category>

<category>cs.CV</category>

<category>cs.HC</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<arxiv:DOI>10.1109/ITSC57777.2023.10422676</arxiv:DOI>

<dc:creator>Leon Eisemann, Mirjam Fehling-Kaschek, Henrik Gommel, David Hermann, Marvin Klemp, Martin Lauer, Benjamin Lickert, Florian Luettner, Robin Moss, Nicole Neis, Maria Pohle, Simon Romanski, Daniel Stadler, Alexander Stolz, Jens Ziehn, Jingxing Zhou</dc:creator>

</item>

<item>

<title>Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming</title>

<link>https://arxiv.org/abs/2405.01787</link>

<description>arXiv:2405.01787v1 Announce Type: cross 

Abstract: Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiability Modulo Theories (SMT) solvers to automate proofs in languages such as F*.

Seeking to spur research on using AI to automate the construction of proof-oriented programs, we curate a dataset of 600K lines of open-source F* programs and proofs, including software used in production systems ranging from Windows and Linux, to Python and Firefox. Our dataset includes around 32K top-level F* definitions, each representing a type-directed program and proof synthesis problem -- producing a definition given a formal specification expressed as an F* type. We provide a program-fragment checker that queries F* to check the correctness of candidate solutions. We believe this is the largest corpus of SMT-assisted program proofs coupled with a reproducible program-fragment checker.

Grounded in this dataset, we investigate the use of AI to synthesize programs and their proofs in F*, with promising results. Our main finding in that the performance of fine-tuned smaller language models (such as Phi-2 or StarCoder) compare favorably with large language models (such as GPT-4), at a much lower computational cost. We also identify various type-based retrieval augmentation techniques and find that they boost performance significantly. With detailed error analysis and case studies, we identify potential strengths and weaknesses of models and techniques and suggest directions for future improvements.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01787v1</guid>

<category>cs.PL</category>

<category>cs.AI</category>

<category>cs.SE</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Saikat Chakraborty, Gabriel Ebner, Siddharth Bhat, Sarah Fakhoury, Sakina Fatima, Shuvendu Lahiri, Nikhil Swamy</dc:creator>

</item>

<item>

<title>Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization</title>

<link>https://arxiv.org/abs/2405.01790</link>

<description>arXiv:2405.01790v1 Announce Type: cross 

Abstract: Text summarization models have typically focused on optimizing aspects of quality such as fluency, relevance, and coherence, particularly in the context of news articles. However, summarization models are increasingly being used to summarize diverse sources of text, such as social media data, that encompass a wide demographic user base. It is thus crucial to assess not only the quality of the generated summaries, but also the extent to which they can fairly represent the opinions of diverse social groups. Position bias, a long-known issue in news summarization, has received limited attention in the context of social multi-document summarization. We deeply investigate this phenomenon by analyzing the effect of group ordering in input documents when summarizing tweets from three distinct linguistic communities: African-American English, Hispanic-aligned Language, and White-aligned Language. Our empirical analysis shows that although the textual quality of the summaries remains consistent regardless of the input document order, in terms of fairness, the results vary significantly depending on how the dialect groups are presented in the input data. Our results suggest that position bias manifests differently in social multi-document summarization, severely impacting the fairness of summarization models.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01790v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Olubusayo Olabisi, Ameeta Agrawal</dc:creator>

</item>

<item>

<title>Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features</title>

<link>https://arxiv.org/abs/2405.01799</link>

<description>arXiv:2405.01799v1 Announce Type: cross 

Abstract: Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state of the art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13% improvement in both accuracy and F1 score in a zero shot learning configuration. This marked enhancement highlights the model potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01799v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Chuanbo Hu, Wenqi Li, Mindi Ruan, Xiangxu Yu, Lynn K. Paul, Shuo Wang, Xin Li</dc:creator>

</item>

<item>

<title>Algorithmic Decision-Making under Agents with Persistent Improvement</title>

<link>https://arxiv.org/abs/2405.01807</link>

<description>arXiv:2405.01807v1 Announce Type: cross 

Abstract: This paper studies algorithmic decision-making under human's strategic behavior, where a decision maker uses an algorithm to make decisions about human agents, and the latter with information about the algorithm may exert effort strategically and improve to receive favorable decisions. Unlike prior works that assume agents benefit from their efforts immediately, we consider realistic scenarios where the impacts of these efforts are persistent and agents benefit from efforts by making improvements gradually. We first develop a dynamic model to characterize persistent improvements and based on this construct a Stackelberg game to model the interplay between agents and the decision-maker. We analytically characterize the equilibrium strategies and identify conditions under which agents have incentives to improve. With the dynamics, we then study how the decision-maker can design an optimal policy to incentivize the largest improvements inside the agent population. We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements. With the extended models, we further examine conditions under which agents prefer honest efforts over dishonest behavior and the impacts of forgettable efforts.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01807v1</guid>

<category>cs.GT</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Tian Xie, Xuwei Tan, Xueru Zhang</dc:creator>

</item>

<item>

<title>Toward end-to-end interpretable convolutional neural networks for waveform signals</title>

<link>https://arxiv.org/abs/2405.01815</link>

<description>arXiv:2405.01815v1 Announce Type: cross 

Abstract: This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability. By benchmarking experiments on three standard speech emotion recognition datasets with five-fold cross-validation, our framework outperforms Mel spectrogram features by up to seven percent. It can potentially replace the Mel-Frequency Cepstral Coefficients (MFCC) while remaining lightweight. Furthermore, we demonstrate the efficiency and interpretability of the front-end layer using the PhysioNet Heart Sound Database, illustrating its ability to handle and capture intricate long waveform patterns. Our contributions offer a portable solution for building efficient and interpretable models for raw waveform data.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01815v1</guid>

<category>cs.SD</category>

<category>cs.AI</category>

<category>eess.AS</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-sa/4.0/</dc:rights>

<dc:creator>Linh Vu, Thu Tran, Wern-Han Lim, Raphael Phan</dc:creator>

</item>

<item>

<title>Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention</title>

<link>https://arxiv.org/abs/2405.01820</link>

<description>arXiv:2405.01820v1 Announce Type: cross 

Abstract: Machine learning systems require representations of the real world for training and testing - they require data, and lots of it. Collecting data at scale has logistical and ethical challenges, and synthetic data promises a solution to these challenges. Instead of needing to collect photos of real people's faces to train a facial recognition system, a model creator could create and use photo-realistic, synthetic faces. The comparative ease of generating this synthetic data rather than relying on collecting data has made it a common practice. We present two key risks of using synthetic data in model development. First, we detail the high risk of false confidence when using synthetic data to increase dataset diversity and representation. We base this in the examination of a real world use-case of synthetic data, where synthetic datasets were generated for an evaluation of facial recognition technology. Second, we examine how using synthetic data risks circumventing consent for data usage. We illustrate this by considering the importance of consent to the U.S. Federal Trade Commission's regulation of data collection and affected models. Finally, we discuss how these two risks exemplify how synthetic data complicates existing governance and ethical practice; by decoupling data from those it impacts, synthetic data is prone to consolidating power away those most impacted by algorithmically-mediated harm.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01820v1</guid>

<category>cs.CY</category>

<category>cs.AI</category>

<category>cs.CV</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<arxiv:DOI>10.1145/3630106.3659002</arxiv:DOI>

<arxiv:journal_reference>FAccT '24, June 03--06, 2024, Rio de Janeiro, Brazil</arxiv:journal_reference>

<dc:creator>Cedric Deslandes Whitney, Justin Norman</dc:creator>

</item>

<item>

<title>Creation of Novel Soft Robot Designs using Generative AI</title>

<link>https://arxiv.org/abs/2405.01824</link>

<description>arXiv:2405.01824v1 Announce Type: cross 

Abstract: Soft robotics has emerged as a promising field with the potential to revolutionize industries such as healthcare and manufacturing. However, designing effective soft robots presents challenges, particularly in managing the complex interplay of material properties, structural design, and control strategies. Traditional design methods are often time-consuming and may not yield optimal designs. In this paper, we explore the use of generative AI to create 3D models of soft actuators. We create a dataset of over 70 text-shape pairings of soft pneumatic robot actuator designs, and adapt a latent diffusion model (SDFusion) to learn the data distribution and generate novel designs from it. By employing transfer learning and data augmentation techniques, we significantly improve the performance of the diffusion model. These findings highlight the potential of generative AI in designing complex soft robotic systems, paving the way for future advancements in the field.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01824v1</guid>

<category>cs.RO</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<dc:creator>Wee Kiat Chan, PengWei Wang, Raye Chen-Hua Yeow</dc:creator>

</item>

<item>

<title>Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization</title>

<link>https://arxiv.org/abs/2405.01843</link>

<description>arXiv:2405.01843v1 Announce Type: cross 

Abstract: The current state-of-the-art theoretical analysis of Actor-Critic (AC) algorithms significantly lags in addressing the practical aspects of AC implementations. This crucial gap needs bridging to bring the analysis in line with practical implementations of AC. To address this, we advocate for considering the MMCLG criteria: \textbf{M}ulti-layer neural network parametrization for actor/critic, \textbf{M}arkovian sampling, \textbf{C}ontinuous state-action spaces, the performance of the \textbf{L}ast iterate, and \textbf{G}lobal optimality. These aspects are practically significant and have been largely overlooked in existing theoretical analyses of AC algorithms. In this work, we address these gaps by providing the first comprehensive theoretical analysis of AC algorithms that encompasses all five crucial practical aspects (covers MMCLG criteria). We establish global convergence sample complexity bounds of $\tilde{\mathcal{O}}\left({\epsilon^{-3}}\right)$. We achieve this result through our novel use of the weak gradient domination property of MDP's and our unique analysis of the error in critic estimation.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01843v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Mudit Gaur, Vaneet Aggarwal, Amrit Singh Bedi, Di Wang</dc:creator>

</item>

<item>

<title>A Model-based Multi-Agent Personalized Short-Video Recommender System</title>

<link>https://arxiv.org/abs/2405.01847</link>

<description>arXiv:2405.01847v1 Announce Type: cross 

Abstract: Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement learning (RL) framework has attracted increasing attention from both academic and industry communities. In this paper, we propose a RL-based industrial short-video recommender ranking framework, which models and maximizes user watch-time in an environment of user multi-aspect preferences by a collaborative multi-agent formulization. Moreover, our proposed framework adopts a model-based learning approach to alleviate the sample selection bias which is a crucial but intractable problem in industrial recommender system. Extensive offline evaluations and live experiments confirm the effectiveness of our proposed method over alternatives. Our proposed approach has been deployed in our real large-scale short-video sharing platform, successfully serving over hundreds of millions users.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01847v1</guid>

<category>cs.IR</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Peilun Zhou, Xiaoxiao Xu, Lantao Hu, Han Li, Peng Jiang</dc:creator>

</item>

<item>

<title>Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls</title>

<link>https://arxiv.org/abs/2405.01851</link>

<description>arXiv:2405.01851v1 Announce Type: cross 

Abstract: There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile devices hold potential to accelerate DL inference via parallel execution across heterogeneous processors. Various efficient parallel methods have been explored to optimize computation distribution, achieve load balance, and minimize communication cost across processors. Yet their practical effectiveness in the dynamic and diverse real-world mobile environment is less explored. This paper presents a holistic empirical study to assess the capabilities and challenges associated with parallel DL inference on heterogeneous mobile processors. Through carefully designed experiments covering various DL models, mobile software/hardware environments, workload patterns, and resource availability, we identify limitations of existing techniques and highlight opportunities for cross-level optimization.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01851v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Sicong Liu, Wentao Zhou, Zimu Zhou, Bin Guo, Minfan Wang, Cheng Fang, Zheng Lin, Zhiwen Yu</dc:creator>

</item>

<item>

<title>AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research</title>

<link>https://arxiv.org/abs/2405.01859</link>

<description>arXiv:2405.01859v1 Announce Type: cross 

Abstract: The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research. This topic has received comparatively little attention of late compared to risks stemming from superintelligent artificial general intelligence (AGI), but requires fewer assumptions about the course of technological development and is thus a nearer-future issue. ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war. In the case of peer adversaries, this increases the likelihood of "low intensity" conflicts which risk escalation to broader warfare. In the case of non-peer adversaries, it reduces the domestic blowback to wars of aggression. This effect can occur regardless of other ethical issues around the use of military AI such as the risk of civilian casualties, and does not require any superhuman AI capabilities. Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research. Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01859v1</guid>

<category>cs.CY</category>

<category>cs.AI</category>

<category>cs.LG</category>

<category>cs.RO</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Riley Simmons-Edler, Ryan Badman, Shayne Longpre, Kanaka Rajan</dc:creator>

</item>

<item>

<title>Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot</title>

<link>https://arxiv.org/abs/2405.01882</link>

<description>arXiv:2405.01882v1 Announce Type: cross 

Abstract: Healthcare monitoring is crucial, especially for the daily care of elderly individuals living alone. It can detect dangerous occurrences, such as falls, and provide timely alerts to save lives. Non-invasive millimeter wave (mmWave) radar-based healthcare monitoring systems using advanced human activity recognition (HAR) models have recently gained significant attention. However, they encounter challenges in handling sparse point clouds, achieving real-time continuous classification, and coping with limited monitoring ranges when statically mounted. To overcome these limitations, we propose RobHAR, a movable robot-mounted mmWave radar system with lightweight deep neural networks for real-time monitoring of human activities. Specifically, we first propose a sparse point cloud-based global embedding to learn the features of point clouds using the light-PointNet (LPN) backbone. Then, we learn the temporal pattern with a bidirectional lightweight LSTM model (BiLiLSTM). In addition, we implement a transition optimization strategy, integrating the Hidden Markov Model (HMM) with Connectionist Temporal Classification (CTC) to improve the accuracy and robustness of the continuous HAR. Our experiments on three datasets indicate that our method significantly outperforms the previous studies in both discrete and continuous HAR tasks. Finally, we deploy our system on a movable robot-mounted edge computing platform, achieving flexible healthcare monitoring in real-world scenarios.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01882v1</guid>

<category>cs.RO</category>

<category>cs.AI</category>

<category>eess.SP</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>

<dc:creator>Zhanzhong Gu, Xiangjian He, Gengfa Fang, Chengpei Xu, Feng Xia, Wenjing Jia</dc:creator>

</item>

<item>

<title>Aloe: A Family of Fine-tuned Open Healthcare LLMs</title>

<link>https://arxiv.org/abs/2405.01886</link>

<description>arXiv:2405.01886v1 Announce Type: cross 

Abstract: As the capabilities of Large Language Models (LLMs) in healthcare and medicine continue to advance, there is a growing need for competitive open-source models that can safeguard public interest. With the increasing availability of highly competitive open base models, the impact of continued pre-training is increasingly uncertain. In this work, we explore the role of instruct tuning, model merging, alignment, red teaming and advanced inference schemes, as means to improve current open models. To that end, we introduce the Aloe family, a set of open medical LLMs highly competitive within its scale range. Aloe models are trained on the current best base models (Mistral, LLaMA 3), using a new custom dataset which combines public data sources improved with synthetic Chain of Thought (CoT). Aloe models undergo an alignment phase, becoming one of the first few policy-aligned open healthcare LLM using Direct Preference Optimization, setting a new standard for ethical performance in healthcare LLMs. Model evaluation expands to include various bias and toxicity datasets, a dedicated red teaming effort, and a much-needed risk assessment for healthcare LLMs. Finally, to explore the limits of current LLMs in inference, we study several advanced prompt engineering strategies to boost performance across benchmarks, yielding state-of-the-art results for open healthcare 7B LLMs, unprecedented at this scale.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01886v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Ashwin Kumar Gururajan, Enrique Lopez-Cuena, Jordi Bayarri-Planas, Adrian Tormos, Daniel Hinjos, Pablo Bernabeu-Perez, Anna Arias-Duart, Pablo Agustin Martin-Torres, Lucia Urcelay-Ganzabal, Marta Gonzalez-Mallo, Sergio Alvarez-Napagao, Eduard Ayguad\'e-Parra, Ulises Cort\'es Dario Garcia-Gasulla</dc:creator>

</item>

<item>

<title>Semi-Parametric Retrieval via Binary Token Index</title>

<link>https://arxiv.org/abs/2405.01924</link>

<description>arXiv:2405.01924v1 Announce Type: cross 

Abstract: The landscape of information retrieval has broadened from search services to a critical component in various advanced applications, where indexing efficiency, cost-effectiveness, and freshness are increasingly important yet remain less explored. To address these demands, we introduce Semi-parametric Vocabulary Disentangled Retrieval (SVDR). SVDR is a novel semi-parametric retrieval framework that supports two types of indexes: an embedding-based index for high effectiveness, akin to existing neural retrieval methods; and a binary token index that allows for quick and cost-effective setup, resembling traditional term-based retrieval. In our evaluation on three open-domain question answering benchmarks with the entire Wikipedia as the retrieval corpus, SVDR consistently demonstrates superiority. It achieves a 3% higher top-1 retrieval accuracy compared to the dense retriever DPR when using an embedding-based index and an 9% higher top-1 accuracy compared to BM25 when using a binary token index. Specifically, the adoption of a binary token index reduces index preparation time from 30 GPU hours to just 2 CPU hours and storage size from 31 GB to 2 GB, achieving a 90% reduction compared to an embedding-based index.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01924v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.IR</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Jiawei Zhou, Li Dong, Furu Wei, Lei Chen</dc:creator>

</item>

<item>

<title>Impact of Architectural Modifications on Deep Learning Adversarial Robustness</title>

<link>https://arxiv.org/abs/2405.01934</link>

<description>arXiv:2405.01934v1 Announce Type: cross 

Abstract: Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are not immune to adversarial manipulations, which can cause the system to misbehave and remain unnoticed by experts. The frequency of modifications to existing deep learning models necessitates thorough analysis to determine the impact on models' robustness. In this work, we present an experimental evaluation of the effects of model modifications on deep learning model robustness using adversarial attacks. Our methodology involves examining the robustness of variations of models against various adversarial attacks. By conducting our experiments, we aim to shed light on the critical issue of maintaining the reliability and safety of deep learning models in safety- and security-critical applications. Our results indicate the pressing demand for an in-depth assessment of the effects of model changes on the robustness of models.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01934v1</guid>

<category>cs.CV</category>

<category>cs.AI</category>

<category>cs.CR</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Firuz Juraev, Mohammed Abuhamad, Simon S. Woo, George K Thiruvathukal, Tamer Abuhmed</dc:creator>

</item>

<item>

<title>Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models</title>

<link>https://arxiv.org/abs/2405.01943</link>

<description>arXiv:2405.01943v1 Announce Type: cross 

Abstract: The rapid advancement in Large Language Models (LLMs) has markedly enhanced the capabilities of language understanding and generation. However, the substantial model size poses hardware challenges, affecting both memory size for serving and inference latency for token generation. To address those challenges, we propose Dependency-aware Semi-structured Sparsity (DaSS), a novel method for the recent prevalent SwiGLU-based LLMs pruning. Our approach incorporates structural dependency into the weight magnitude-based unstructured pruning. We introduce an MLP-specific pruning metric that evaluates the importance of each weight by jointly considering its magnitude and its corresponding MLP intermediate activation norms. DaSS facilitates a balance between the adaptability offered by unstructured pruning and the structural consistency inherent in dependency-based structured pruning. Empirical evaluations on Mistral and LLaMA2 model families demonstrate that DaSS not only outperforms both SparseGPT and Wanda in achieving hardware-friendly N:M sparsity patterns but also maintains the computational efficiency of Wanda.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01943v1</guid>

<category>cs.CL</category>

<category>cs.AI</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Zhiyu Guo, Hidetaka Kamigaito, Taro Wanatnabe</dc:creator>

</item>

<item>

<title>Three Quantization Regimes for ReLU Networks</title>

<link>https://arxiv.org/abs/2405.01952</link>

<description>arXiv:2405.01952v1 Announce Type: cross 

Abstract: We establish the fundamental limits in the approximation of Lipschitz functions by deep ReLU neural networks with finite-precision weights. Specifically, three regimes, namely under-, over-, and proper quantization, in terms of minimax approximation error behavior as a function of network weight precision, are identified. This is accomplished by deriving nonasymptotic tight lower and upper bounds on the minimax approximation error. Notably, in the proper-quantization regime, neural networks exhibit memory-optimality in the approximation of Lipschitz functions. Deep networks have an inherent advantage over shallow networks in achieving memory-optimality. We also develop the notion of depth-precision tradeoff, showing that networks with high-precision weights can be converted into functionally equivalent deeper networks with low-precision weights, while preserving memory-optimality. This idea is reminiscent of sigma-delta analog-to-digital conversion, where oversampling rate is traded for resolution in the quantization of signal samples. We improve upon the best-known ReLU network approximation results for Lipschitz functions and describe a refinement of the bit extraction technique which could be of independent general interest.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01952v1</guid>

<category>stat.ML</category>

<category>cs.AI</category>

<category>cs.IT</category>

<category>cs.LG</category>

<category>math.IT</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>

<dc:creator>Weigutian Ou, Philipp Schenkel, Helmut B\"olcskei</dc:creator>

</item>

<item>

<title>From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings</title>

<link>https://arxiv.org/abs/2405.01963</link>

<description>arXiv:2405.01963v1 Announce Type: cross 

Abstract: Deep Learning (DL) is rapidly maturing to the point that it can be used in safety- and security-crucial applications. However, adversarial samples, which are undetectable to the human eye, pose a serious threat that can cause the model to misbehave and compromise the performance of such applications. Addressing the robustness of DL models has become crucial to understanding and defending against adversarial attacks. In this study, we perform comprehensive experiments to examine the effect of adversarial attacks and defenses on various model architectures across well-known datasets. Our research focuses on black-box attacks such as SimBA, HopSkipJump, MGAAttack, and boundary attacks, as well as preprocessor-based defensive mechanisms, including bits squeezing, median smoothing, and JPEG filter. Experimenting with various models, our results demonstrate that the level of noise needed for the attack increases as the number of layers increases. Moreover, the attack success rate decreases as the number of layers increases. This indicates that model complexity and robustness have a significant relationship. Investigating the diversity and robustness relationship, our experiments with diverse models show that having a large number of parameters does not imply higher robustness. Our experiments extend to show the effects of the training dataset on model robustness. Using various datasets such as ImageNet-1000, CIFAR-100, and CIFAR-10 are used to evaluate the black-box attacks. Considering the multiple dimensions of our analysis, e.g., model complexity and training dataset, we examined the behavior of black-box attacks when models apply defenses. Our results show that applying defense strategies can significantly reduce attack effectiveness. This research provides in-depth analysis and insight into the robustness of DL models against various attacks, and defenses.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01963v1</guid>

<category>cs.CR</category>

<category>cs.AI</category>

<category>cs.CV</category>

<category>cs.LG</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Firuz Juraev, Mohammed Abuhamad, Eric Chan-Tin, George K. Thiruvathukal, Tamer Abuhmed</dc:creator>

</item>

<item>

<title>Multitask Extension of Geometrically Aligned Transfer Encoder</title>

<link>https://arxiv.org/abs/2405.01974</link>

<description>arXiv:2405.01974v1 Announce Type: cross 

Abstract: Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we leverage mutual information across different tasks in molecular data to address this issue. We extend an algorithm that utilizes the geometric characteristics of the encoding space, known as the Geometrically Aligned Transfer Encoder (GATE), to a multi-task setup. Thus, we connect multiple molecular tasks by aligning the curved coordinates onto locally flat coordinates, ensuring the flow of information from source tasks to support performance on target data.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01974v1</guid>

<category>cs.LG</category>

<category>cs.AI</category>

<category>q-bio.QM</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>

<dc:creator>Sung Moon Ko, Sumin Lee, Dae-Woong Jeong, Hyunseung Kim, Chanhui Lee, Soorin Yim, Sehui Han</dc:creator>

</item>

<item>

<title>A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints</title>

<link>https://arxiv.org/abs/2405.01984</link>

<description>arXiv:2405.01984v1 Announce Type: cross 

Abstract: Traditional mathematical programming solvers require long computational times to solve constrained minimization problems of complex and large-scale physical systems. Therefore, these problems are often transformed into unconstrained ones, and solved with computationally efficient optimization approaches based on first-order information, such as the gradient descent method. However, for unconstrained problems, balancing the minimization of the objective function with the reduction of constraint violations is challenging. We consider the class of time-dependent minimization problems with increasing (possibly) nonlinear and non-convex objective function and non-decreasing (possibly) nonlinear and non-convex inequality constraints. To efficiently solve them, we propose a penalty-based guardrail algorithm (PGA). This algorithm adapts a standard penalty-based method by dynamically updating the right-hand side of the constraints with a guardrail variable which adds a margin to prevent violations. We evaluate PGA on two novel application domains: a simplified model of a district heating system and an optimization model derived from learned deep neural networks. Our method significantly outperforms mathematical programming solvers and the standard penalty-based method, and achieves better performance and faster convergence than a state-of-the-art algorithm (IPDD) within a specified time limit.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01984v1</guid>

<category>math.OC</category>

<category>cs.AI</category>

<arxiv:announce_type>cross</arxiv:announce_type>

<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>

<dc:creator>Ksenija Stepanovic, Wendelin B\"ohmer, Mathijs de Weerdt</dc:creator>

</item>

<item>

<title>Joint sentiment analysis of lyrics and audio in music</title>

<link>https://arxiv.org/abs/2405.01988</link>

<description>arXiv:2405.01988v1 Announce Type: cross 

Abstract: Sentiment or mood can express themselves on various levels in music. In automatic analysis, the actual audio data is usually analyzed, but the lyrics can also play a crucial role in the perception of moods. We first evaluate various models for sentiment analysis based on lyrics and audio separately. The corresponding approaches already show satisfactory results, but they also exhibit weaknesses, the causes of which we examine in more detail. Furthermore, different approaches to combining the audio and lyrics results are proposed and evaluated. Considering both modalities generally leads to improved performance. We investigate misclassifications and (also intentional) contradictions between audio and lyrics sentiment more closely, and identify possible causes. Finally, we address fundamental problems in this research area, such as high subjectivity, lack of data, and inconsistency in emotion taxonomies.</description>

<guid isPermaLink="false">oai:arXiv.org:2405.01988v1</guid>

<category>cs.SD</category>

<category>cs.AI</category>

<category>cs.CL</category>

<category>cs.LG</category>

<category>eess.AS</category>

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