On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models

Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati·May 22, 2024

Summary

This collection of papers investigates the effectiveness of ReAct-based prompting in enhancing large language models' (LLMs) sequential decision-making abilities, particularly in the context of the AlfWorld environment. The studies question the claim that ReAct's interleaving of reasoning and action execution improves performance, finding that it is primarily due to the high similarity between input examples and queries, suggesting a reliance on approximate retrieval rather than inherent reasoning. LLMs are shown to be brittle, with performance being sensitive to prompt variations and exemplar-task similarity. The research also explores different prompting strategies, such as exemplar-based and anonymized guidance, and evaluates the performance of models like GPT-3.5, GPT-4, and Claude-Opus, revealing their limitations and the need for more nuanced understanding of their reasoning capabilities and the role of prompt engineering in their performance.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper focuses on the brittleness of prompt engineering in enhancing the reasoning abilities of Large Language Models (LLMs) like GPT-3-Turbo, particularly in the context of ReAct prompting . It aims to address the challenges and limitations associated with prompt engineering, highlighting the fragility of the system when minor perturbations are introduced to the input prompt . This problem of brittleness in prompt engineering is not entirely new, as it has been a subject of investigation in the context of LLMs and their reasoning capabilities . The paper delves into the intricacies of prompt design and its impact on the performance and reliability of LLMs in planning and reasoning tasks, shedding light on the complexities and shortcomings of current prompting methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the performance and claims of ReAct-based Large Language Models (LLMs) in sequential decision-making tasks. The study investigates the components of ReAct, such as interleaving thinking with acting, plan guidance, and exemplar prompt selection, to understand their impact on LLM agents' performance . The research questions focus on aspects like the importance of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the effectiveness of different prompt variations . The paper delves into the brittleness of ReAct to assess whether its fundamental assertions hold true, particularly in the context of sequential decision-making problems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" introduces several new ideas, methods, and models related to prompting in large language models (LLMs) . Here are some key points from the paper:

  1. ReAct Prompting: The paper discusses ReAct prompting, which aims to enhance the reasoning and acting abilities of LLMs through specific prompt variations . These prompt variations are designed to improve the performance of LLMs in tasks that require reasoning and problem-solving skills.

  2. Exemplar Chain of Thought (CoT): The study evaluates the effectiveness of exemplar Chain of Thought in LLMs and tests various prompt variations, such as RQ3-Both, RQ3-One, and RQ3-Exemplar CoT . The results show varying performance levels based on the prompt variation used, indicating the importance of prompt design in influencing LLM behavior.

  3. New Models and Approaches: The paper references newer LLM models like GPT-3.5-Turbo, GPT-3.5-Instruct, GPT-4, and Claude-Opus, which are used in the experiments to evaluate the impact of different prompt settings . These models are compared based on their performance across various tasks in the AlfWorld domain.

  4. Prompt Engineering: The study delves into prompt engineering efforts, such as ReAct, and questions claims of enhanced "emergent reasoning" in LLMs through prompt design . It highlights the challenges and limitations associated with prompt engineering and its impact on the reasoning abilities of LLMs.

  5. Sensitivity Analysis: The paper conducts sensitivity analysis using proposed prompt variations along different dimensions, such as the location and content of the think tag . This analysis helps in understanding how variations in prompts can affect the performance of LLMs in reasoning tasks.

Overall, the paper contributes to the ongoing research on enhancing the reasoning capabilities of large language models through innovative prompt design, evaluation of new LLM models, and sensitivity analysis of prompt variations in different tasks and domains. The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" introduces novel concepts and challenges existing methods in prompting for large language models (LLMs) . Here are some key characteristics and advantages compared to previous methods based on the details in the paper:

  1. ReAct Prompting: The study evaluates ReAct prompting, which aims to enhance the sequential decision-making abilities of agentic LLMs through interleaving reasoning trace with action execution and providing guidance information following the think tag . However, the paper questions the effectiveness of ReAct in improving LLM performance, highlighting that the success of LLMs in sequential decision-making tasks is more influenced by the similarity between exemplar problems and queries rather than the prompt design .

  2. Exemplar Chain of Thought (CoT): The paper explores the utility of exemplar CoT variations in LLMs and compares them to base ReAct prompting. It is noted that exemplar CoT and anonymized exemplar CoT perform better than base ReAct for GPT-X family models, indicating potential advantages in performance .

  3. Prompt Engineering Challenges: The study raises concerns about prompt engineering efforts like ReAct, emphasizing the limitations of scaling instance-specific examples in domains with numerous problem classes . This highlights the burden prompt engineers face in providing tailored examples for effective prompt design.

  4. Sensitivity Analysis: The paper conducts sensitivity analysis by proposing variations along different dimensions, such as interleaving thinking with acting and guidance information following the think tag . This analysis helps in understanding the impact of prompt variations on LLM performance in sequential decision-making tasks.

  5. Brittleness of ReAct: The research delves into the brittleness of ReAct-based agentic LLMs, questioning the fundamental assertions of ReAct in sequential decision-making . It emphasizes that the performance of LLMs is not solely dependent on interleaving reasoning trace and action execution but rather on exemplar-query similarity and approximate retrieval.

In summary, the paper sheds light on the limitations of ReAct prompting, challenges the claims of enhanced reasoning abilities in LLMs through prompt engineering, and underscores the importance of exemplar-query similarity in driving LLM performance in sequential decision-making tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of large language models and prompt engineering. Noteworthy researchers in this area include Renat Aksitov, Maciej Besta, Sébastien Bubeck, Amrita Bhattacharjee, and many others . One key aspect highlighted in the research is the brittleness of prompt engineering, cautioning against overreliance on React in enhancing the reasoning abilities of Large Language Models (LLMs) . The papers discuss various techniques, applications, and investigations related to prompt engineering, reasoning, and planning abilities of LLMs, shedding light on the challenges and limitations in this domain .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the effectiveness of different variations of exemplar prompts in the context of ReAct prompting for large language models . The experiment design involved modifying the few-shot examples while keeping other aspects such as the query problem and interaction with the simulator inherited from the ReAct code-base . Each variation proposed along RQ1, RQ2, and RQ3 focused on altering the content of the exemplar prompts . The experiments were run according to the variation style, using the same exemplar prompts across instances of the query task, except for specific variations like RQ3-Both and One, which involved changing the content of the exemplars .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ReAct code-base by Yao et al. [2022], which is publicly available on GitHub at https://github.com/ysymyth/ReAct . The code used for the experiments is open source and can be found in the attached supplementary material of the study .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study extensively explores the effectiveness of different variations in prompting large language models (LLMs) and their impact on performance . The research questions addressed in the study, such as the importance of interleaving reasoning trace with action execution and the utility of guidance information, are thoroughly investigated through experiments . The findings reveal significant insights into the brittleness of prompt engineering and the challenges faced by LLMs in operationalizing generated thoughts . Additionally, the study delves into the dependence of LLMs on the similarity of exemplars to the query task, highlighting the importance of instance-specific exemplars in enhancing reasoning abilities .

The results from the experiments demonstrate the impact of different prompt variations on LLM performance across various tasks . The study systematically evaluates the success rates of different LLM models under various prompt settings, providing a comprehensive analysis of the effectiveness of each variation . Furthermore, the research investigates the role of guidance information in prompting LLMs and its influence on decision-making tasks, shedding light on the strengths and weaknesses of different prompt engineering strategies . Overall, the experiments conducted in the paper offer valuable insights into the design and optimization of prompts for enhancing the reasoning capabilities of large language models .


What are the contributions of this paper?

The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" makes several contributions:

  • It investigates the performance of ReAct-based Large Language Model (LLM) agents by examining the claims of ReAct in sequential decision-making scenarios .
  • The paper proposes a sensitivity analysis by suggesting alternatives along three dimensions of ReAct: interleaving the think tag with actions, plan guidance after the think tag, and the selection of exemplar problems for LLM prompts .
  • It explores the design of exemplar prompt variations to assess the effectiveness of ReAct and investigates research questions related to the claims of ReAct .
  • The study evaluates the impact of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the performance of different variations of ReAct on the success rates of various tasks .
  • The findings challenge the importance of interleaving reasoning trace generation with action execution as claimed by ReAct, highlighting the performance differences across different variations of ReAct and questioning the significance of certain components in enhancing LLM performance .

What work can be continued in depth?

Further research can be conducted to delve deeper into the performance implications of ReAct-based prompting on Large Language Models (LLMs). Specifically, exploring the impact of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the similarity between input example tasks and queries could provide valuable insights . Additionally, investigating the brittleness of LLMs in planning tasks, the operationalization ability of LLMs in generating reasonable thoughts, and the dependence of LLMs on the similarity of exemplars to the query task could be areas of continued study . These avenues of research could contribute to a better understanding of the reasoning abilities and performance factors of LLMs in sequential decision-making tasks.


Introduction
Background
Overview of ReAct and its role in sequential decision-making
AlfWorld environment and its significance
Objective
To examine the effectiveness of ReAct in LLMs
Challenge the claim of improved performance through reasoning-action interleaving
Investigate reliance on retrieval vs. inherent reasoning
Method
Data Collection
Selection of LLMs (GPT-3.5, GPT-4, Claude-Opus)
alfWorld dataset and input/output examples
Collection of ReAct and alternative prompting strategies
Data Preprocessing
Standardization of input prompts and queries
Analysis of prompt similarity and variation
Identifying exemplar-task relationships
Experiment Design
Controlled experiments with ReAct and non-ReAct prompts
Evaluation of model performance under different conditions
Performance Metrics
Task completion rates
Sensitivity to prompt changes
Retrieval vs. reasoning-based performance
Analysis
Breakdown of results by model architecture
Exemplar-based vs. anonymized guidance comparison
Identifying factors influencing performance
Results
ReAct's effectiveness: empirical evidence and limitations
Retrieval bias in LLMs
Performance variability across models
Discussion
Interpretation of findings in the context of reasoning and action execution
Implications for prompt engineering and model design
Future directions for research on LLM reasoning capabilities
Conclusion
Summary of key findings
The role of prompt design in shaping LLM performance
Recommendations for future research on sequential decision-making in LLMs
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
Which language models are evaluated in the studies, and what are the limitations discovered for these models?
What is the main finding regarding LLMs' performance sensitivity to prompt variations and exemplar-task similarity?
How does the research challenge the effectiveness of ReAct-based prompting in the AlfWorld environment?
What does the collection of papers focus on in terms of improving LLMs' decision-making abilities?

On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models

Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati·May 22, 2024

Summary

This collection of papers investigates the effectiveness of ReAct-based prompting in enhancing large language models' (LLMs) sequential decision-making abilities, particularly in the context of the AlfWorld environment. The studies question the claim that ReAct's interleaving of reasoning and action execution improves performance, finding that it is primarily due to the high similarity between input examples and queries, suggesting a reliance on approximate retrieval rather than inherent reasoning. LLMs are shown to be brittle, with performance being sensitive to prompt variations and exemplar-task similarity. The research also explores different prompting strategies, such as exemplar-based and anonymized guidance, and evaluates the performance of models like GPT-3.5, GPT-4, and Claude-Opus, revealing their limitations and the need for more nuanced understanding of their reasoning capabilities and the role of prompt engineering in their performance.
Mind map
Retrieval vs. reasoning-based performance
Sensitivity to prompt changes
Task completion rates
Evaluation of model performance under different conditions
Controlled experiments with ReAct and non-ReAct prompts
Identifying factors influencing performance
Exemplar-based vs. anonymized guidance comparison
Breakdown of results by model architecture
Performance Metrics
Experiment Design
Collection of ReAct and alternative prompting strategies
alfWorld dataset and input/output examples
Selection of LLMs (GPT-3.5, GPT-4, Claude-Opus)
Investigate reliance on retrieval vs. inherent reasoning
Challenge the claim of improved performance through reasoning-action interleaving
To examine the effectiveness of ReAct in LLMs
AlfWorld environment and its significance
Overview of ReAct and its role in sequential decision-making
Recommendations for future research on sequential decision-making in LLMs
The role of prompt design in shaping LLM performance
Summary of key findings
Future directions for research on LLM reasoning capabilities
Implications for prompt engineering and model design
Interpretation of findings in the context of reasoning and action execution
Performance variability across models
Retrieval bias in LLMs
ReAct's effectiveness: empirical evidence and limitations
Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Overview of ReAct and its role in sequential decision-making
AlfWorld environment and its significance
Objective
To examine the effectiveness of ReAct in LLMs
Challenge the claim of improved performance through reasoning-action interleaving
Investigate reliance on retrieval vs. inherent reasoning
Method
Data Collection
Selection of LLMs (GPT-3.5, GPT-4, Claude-Opus)
alfWorld dataset and input/output examples
Collection of ReAct and alternative prompting strategies
Data Preprocessing
Standardization of input prompts and queries
Analysis of prompt similarity and variation
Identifying exemplar-task relationships
Experiment Design
Controlled experiments with ReAct and non-ReAct prompts
Evaluation of model performance under different conditions
Performance Metrics
Task completion rates
Sensitivity to prompt changes
Retrieval vs. reasoning-based performance
Analysis
Breakdown of results by model architecture
Exemplar-based vs. anonymized guidance comparison
Identifying factors influencing performance
Results
ReAct's effectiveness: empirical evidence and limitations
Retrieval bias in LLMs
Performance variability across models
Discussion
Interpretation of findings in the context of reasoning and action execution
Implications for prompt engineering and model design
Future directions for research on LLM reasoning capabilities
Conclusion
Summary of key findings
The role of prompt design in shaping LLM performance
Recommendations for future research on sequential decision-making in LLMs

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper focuses on the brittleness of prompt engineering in enhancing the reasoning abilities of Large Language Models (LLMs) like GPT-3-Turbo, particularly in the context of ReAct prompting . It aims to address the challenges and limitations associated with prompt engineering, highlighting the fragility of the system when minor perturbations are introduced to the input prompt . This problem of brittleness in prompt engineering is not entirely new, as it has been a subject of investigation in the context of LLMs and their reasoning capabilities . The paper delves into the intricacies of prompt design and its impact on the performance and reliability of LLMs in planning and reasoning tasks, shedding light on the complexities and shortcomings of current prompting methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the performance and claims of ReAct-based Large Language Models (LLMs) in sequential decision-making tasks. The study investigates the components of ReAct, such as interleaving thinking with acting, plan guidance, and exemplar prompt selection, to understand their impact on LLM agents' performance . The research questions focus on aspects like the importance of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the effectiveness of different prompt variations . The paper delves into the brittleness of ReAct to assess whether its fundamental assertions hold true, particularly in the context of sequential decision-making problems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" introduces several new ideas, methods, and models related to prompting in large language models (LLMs) . Here are some key points from the paper:

  1. ReAct Prompting: The paper discusses ReAct prompting, which aims to enhance the reasoning and acting abilities of LLMs through specific prompt variations . These prompt variations are designed to improve the performance of LLMs in tasks that require reasoning and problem-solving skills.

  2. Exemplar Chain of Thought (CoT): The study evaluates the effectiveness of exemplar Chain of Thought in LLMs and tests various prompt variations, such as RQ3-Both, RQ3-One, and RQ3-Exemplar CoT . The results show varying performance levels based on the prompt variation used, indicating the importance of prompt design in influencing LLM behavior.

  3. New Models and Approaches: The paper references newer LLM models like GPT-3.5-Turbo, GPT-3.5-Instruct, GPT-4, and Claude-Opus, which are used in the experiments to evaluate the impact of different prompt settings . These models are compared based on their performance across various tasks in the AlfWorld domain.

  4. Prompt Engineering: The study delves into prompt engineering efforts, such as ReAct, and questions claims of enhanced "emergent reasoning" in LLMs through prompt design . It highlights the challenges and limitations associated with prompt engineering and its impact on the reasoning abilities of LLMs.

  5. Sensitivity Analysis: The paper conducts sensitivity analysis using proposed prompt variations along different dimensions, such as the location and content of the think tag . This analysis helps in understanding how variations in prompts can affect the performance of LLMs in reasoning tasks.

Overall, the paper contributes to the ongoing research on enhancing the reasoning capabilities of large language models through innovative prompt design, evaluation of new LLM models, and sensitivity analysis of prompt variations in different tasks and domains. The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" introduces novel concepts and challenges existing methods in prompting for large language models (LLMs) . Here are some key characteristics and advantages compared to previous methods based on the details in the paper:

  1. ReAct Prompting: The study evaluates ReAct prompting, which aims to enhance the sequential decision-making abilities of agentic LLMs through interleaving reasoning trace with action execution and providing guidance information following the think tag . However, the paper questions the effectiveness of ReAct in improving LLM performance, highlighting that the success of LLMs in sequential decision-making tasks is more influenced by the similarity between exemplar problems and queries rather than the prompt design .

  2. Exemplar Chain of Thought (CoT): The paper explores the utility of exemplar CoT variations in LLMs and compares them to base ReAct prompting. It is noted that exemplar CoT and anonymized exemplar CoT perform better than base ReAct for GPT-X family models, indicating potential advantages in performance .

  3. Prompt Engineering Challenges: The study raises concerns about prompt engineering efforts like ReAct, emphasizing the limitations of scaling instance-specific examples in domains with numerous problem classes . This highlights the burden prompt engineers face in providing tailored examples for effective prompt design.

  4. Sensitivity Analysis: The paper conducts sensitivity analysis by proposing variations along different dimensions, such as interleaving thinking with acting and guidance information following the think tag . This analysis helps in understanding the impact of prompt variations on LLM performance in sequential decision-making tasks.

  5. Brittleness of ReAct: The research delves into the brittleness of ReAct-based agentic LLMs, questioning the fundamental assertions of ReAct in sequential decision-making . It emphasizes that the performance of LLMs is not solely dependent on interleaving reasoning trace and action execution but rather on exemplar-query similarity and approximate retrieval.

In summary, the paper sheds light on the limitations of ReAct prompting, challenges the claims of enhanced reasoning abilities in LLMs through prompt engineering, and underscores the importance of exemplar-query similarity in driving LLM performance in sequential decision-making tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of large language models and prompt engineering. Noteworthy researchers in this area include Renat Aksitov, Maciej Besta, Sébastien Bubeck, Amrita Bhattacharjee, and many others . One key aspect highlighted in the research is the brittleness of prompt engineering, cautioning against overreliance on React in enhancing the reasoning abilities of Large Language Models (LLMs) . The papers discuss various techniques, applications, and investigations related to prompt engineering, reasoning, and planning abilities of LLMs, shedding light on the challenges and limitations in this domain .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the effectiveness of different variations of exemplar prompts in the context of ReAct prompting for large language models . The experiment design involved modifying the few-shot examples while keeping other aspects such as the query problem and interaction with the simulator inherited from the ReAct code-base . Each variation proposed along RQ1, RQ2, and RQ3 focused on altering the content of the exemplar prompts . The experiments were run according to the variation style, using the same exemplar prompts across instances of the query task, except for specific variations like RQ3-Both and One, which involved changing the content of the exemplars .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ReAct code-base by Yao et al. [2022], which is publicly available on GitHub at https://github.com/ysymyth/ReAct . The code used for the experiments is open source and can be found in the attached supplementary material of the study .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study extensively explores the effectiveness of different variations in prompting large language models (LLMs) and their impact on performance . The research questions addressed in the study, such as the importance of interleaving reasoning trace with action execution and the utility of guidance information, are thoroughly investigated through experiments . The findings reveal significant insights into the brittleness of prompt engineering and the challenges faced by LLMs in operationalizing generated thoughts . Additionally, the study delves into the dependence of LLMs on the similarity of exemplars to the query task, highlighting the importance of instance-specific exemplars in enhancing reasoning abilities .

The results from the experiments demonstrate the impact of different prompt variations on LLM performance across various tasks . The study systematically evaluates the success rates of different LLM models under various prompt settings, providing a comprehensive analysis of the effectiveness of each variation . Furthermore, the research investigates the role of guidance information in prompting LLMs and its influence on decision-making tasks, shedding light on the strengths and weaknesses of different prompt engineering strategies . Overall, the experiments conducted in the paper offer valuable insights into the design and optimization of prompts for enhancing the reasoning capabilities of large language models .


What are the contributions of this paper?

The paper "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models" makes several contributions:

  • It investigates the performance of ReAct-based Large Language Model (LLM) agents by examining the claims of ReAct in sequential decision-making scenarios .
  • The paper proposes a sensitivity analysis by suggesting alternatives along three dimensions of ReAct: interleaving the think tag with actions, plan guidance after the think tag, and the selection of exemplar problems for LLM prompts .
  • It explores the design of exemplar prompt variations to assess the effectiveness of ReAct and investigates research questions related to the claims of ReAct .
  • The study evaluates the impact of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the performance of different variations of ReAct on the success rates of various tasks .
  • The findings challenge the importance of interleaving reasoning trace generation with action execution as claimed by ReAct, highlighting the performance differences across different variations of ReAct and questioning the significance of certain components in enhancing LLM performance .

What work can be continued in depth?

Further research can be conducted to delve deeper into the performance implications of ReAct-based prompting on Large Language Models (LLMs). Specifically, exploring the impact of interleaving reasoning trace with action execution, the utility of guidance information following the think tag, and the similarity between input example tasks and queries could provide valuable insights . Additionally, investigating the brittleness of LLMs in planning tasks, the operationalization ability of LLMs in generating reasonable thoughts, and the dependence of LLMs on the similarity of exemplars to the query task could be areas of continued study . These avenues of research could contribute to a better understanding of the reasoning abilities and performance factors of LLMs in sequential decision-making tasks.

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