Chain of Thoughtlessness? An Analysis of CoT in Planning

Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati·May 08, 2024

Summary

This paper investigates the effectiveness of chain of thought (CoT) prompting in improving large language models (LLMs) for reasoning tasks, particularly in Blocksworld and other synthetic domains. The study finds that CoT can lead to performance gains when prompts are tailored to specific problems, but these gains are limited and diminish as problem complexity increases. The improvements are not due to the model learning general algorithms but rather rely on problem-specific prompts, highlighting the tradeoff between potential benefits and the need for extensive human effort in creating such prompts. The research suggests that CoT's effectiveness may be less robust than previously thought and questions its general applicability, emphasizing the importance of understanding the limitations and the role of prompt design in LLM performance.

Key findings

5

Paper digest

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

The paper focuses on analyzing the effectiveness of Chain of Thought (CoT) prompts in planning tasks, specifically in the context of Blocksworld problems . The study evaluates the performance of Large Language Models (LLMs) like GPT-4 and Claude-3-Opus when provided with different types of prompts, including zero-shot CoT, domain-specific n-shot, and progression proof CoT . The research aims to understand how well LLMs can apply advice beyond specific instances and how effective they are in problem-solving with the guidance of CoT prompts .

The problem addressed in the paper is not entirely new, as it builds upon previous work on CoT and LLMs in planning and reasoning tasks . The study extends the evaluation of CoT to scalable synthetic benchmarks and considers subsets of Blocksworld problems to assess the generalization capabilities of LLMs with CoT prompts . While the research delves into the nuances of CoT prompting strategies and their impact on problem-solving performance, it does not introduce a fundamentally new problem but rather explores the application of existing techniques in a specific domain .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that Chain-of-Thought (CoT) prompting can enhance reasoning capabilities in large language models, particularly in the context of classical planning problems . The study explores the effectiveness of CoT in enabling models to learn how to reason and plan, with a focus on out-of-domain generalization . The research delves into the relationship between presented chains of thought and the final answers, as well as the impact of prompt specificity on model performance . Additionally, the paper investigates the brittleness and limitations of language models in reasoning and planning tasks, aiming to shed light on the potential of CoT prompting to improve these capabilities .


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

The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" proposes several new ideas, methods, and models related to language models and reasoning abilities . One key concept introduced is the Chain of Thought (CoT), which has become widely adopted for enhancing planning and reasoning skills in Large Language Models (LLMs) . The study questions the effectiveness of operationalizing and generalizing CoT advice in LLMs, suggesting that CoT is only effective when LLMs can perform straightforward pattern matching between examples and problems .

The paper highlights the importance of prompt engineering in improving LLM performance without retraining, emphasizing the capability of LLMs for powerful in-context learning . It discusses the challenges associated with crafting specific prompts that work consistently within a narrow problem class, indicating that very specific prompts may require significant human labor to create but are more likely to be effective .

Furthermore, the study evaluates the Self Consistency extension of CoT on table to stack problems and finds that it does not lead to a generalization breakthrough and may perform worse than the original results in certain scenarios . This analysis sheds light on the limitations of language models in tasks requiring planning and reasoning, cautioning against false confidence in the application of LLMs to such tasks .

Overall, the paper contributes to the understanding of the effectiveness and limitations of CoT methodology in enhancing planning and reasoning abilities of LLMs, emphasizing the need for careful consideration of prompt design and the generalizability of learned procedures across different problem instances . The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" introduces the Chain of Thought (CoT) approach, which aims to enhance planning and reasoning abilities in Large Language Models (LLMs) by providing human-crafted "thoughts" for the LLM to imitate in its response . Compared to previous methods, CoT methodology relies on human labor to provide task-specific knowledge and algorithmic approaches, which can lead to improved performance in complex reasoning tasks . However, the effectiveness of CoT is contingent on the specificity and granularity of the provided knowledge, with very specific prompts showing higher performance but requiring significant human effort to craft .

One key advantage of the CoT approach is its potential to unlock the reasoning abilities of LLMs by teaching them algorithmic procedures through annotated examples, leading to performance enhancements in planning and reasoning tasks . The paper highlights that CoT prompts, when properly constructed, can teach LLMs to generalize basic algorithmic procedures across a large class of problems, thereby converting a modest amount of human teaching effort into a significant capability boost . Additionally, CoT prompts have been shown to induce in-context learning, enabling LLMs to intelligently use additional context provided in prompts to respond correctly to queries .

Furthermore, the study emphasizes the importance of prompt engineering in improving LLM performance without retraining, with CoT being a foundational method for inducing in-context learning . By providing a structured approach to prompt design, CoT prompts aim to guide LLMs through intermediate reasoning steps, allowing them to learn and apply procedures effectively in various problem instances . This method contrasts with previous approaches that may lack systematic scalability or fail to generalize effectively across different problem sets .

In summary, the characteristics and advantages of the Chain of Thought approach, as outlined in the paper, include its reliance on human-crafted "thoughts" to teach LLMs algorithmic procedures, its potential to unlock reasoning abilities and improve performance in planning tasks, and its focus on prompt engineering for in-context learning and generalization across problem instances .


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 studies exist in the field of chain-of-thought reasoning and planning. Noteworthy researchers in this area include Guangsheng Bao, Hongbo Zhang, Linyi Yang, Cunxiang Wang, and Yue Zhang . Additionally, other prominent researchers contributing to this field include Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant . The key to the solution mentioned in the paper involves the utilization of large language models (LLMs) for in-context learning, enabling them to intelligently use additional context provided in a prompt to correctly respond to queries that would otherwise be answered incorrectly .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of GPT-4 and Claude-3-Opus on Blocksworld problems using different prompting methods . The experiments involved testing the models with standard 2-shot prompts and chain of thought prompts of varying granularity, specifically tailored to the intended problem class . The study aimed to assess the impact of chain of thought prompting on problem-solving abilities, particularly in planning domains like Blocksworld, where the difficulty scales with the number of blocks involved . The results indicated that while chain of thought advice can enhance performance on narrow problem distributions, its effectiveness diminishes as the level of specificity increases, highlighting the challenges associated with providing detailed guidance for complex tasks .


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

The dataset used for quantitative evaluation in the study on chain-of-thought in planning tasks is not explicitly mentioned in the provided context . Additionally, there is no information provided regarding the open-source availability of the code used in the research.


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 valuable insights into the effectiveness of chain of thought (CoT) in planning tasks, shedding light on the limitations and challenges associated with this approach . The analysis conducted on the performance of large language models (LLMs) like GPT-4 and Claude-3-Opus in planning domains demonstrates that while CoT may offer some improvements in specific problem distributions, its effectiveness diminishes as the complexity of the problems increases . The study highlights that the ability of LLMs to generalize and reason across different scenarios is limited, especially when faced with tasks requiring multiple reasoning steps or modifications in the problem domain .

Moreover, the research emphasizes the importance of rigorous evaluation mechanisms, urging the scientific community to adopt more comprehensive testing approaches, particularly when assessing the algorithmic reasoning capabilities of black box models like LLMs . The findings suggest that while LLMs may excel in specific problem sets or with basic pattern matching, their performance in handling arbitrary new instances of increasing difficulty remains a challenge . This underscores the need for a deeper understanding of how LLMs learn and apply procedural reasoning, as well as the necessity for robust evaluation frameworks to draw reliable conclusions about their capabilities .

In conclusion, the experiments and results presented in the paper provide valuable insights into the challenges and limitations of employing chain of thought in planning tasks using large language models. The analysis underscores the need for further research to enhance the generalization and reasoning abilities of LLMs, emphasizing the importance of rigorous evaluation methods to ensure the reliability of conclusions drawn about their performance in complex problem-solving scenarios .


What are the contributions of this paper?

The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" makes several contributions:

  • It presents a case study on the performance of Large Language Models (LLMs) using chain of thought prompting on problems from Blocksworld, a classical planning domain, focusing on the generality of examples given in prompts and the complexity of problems queried with each prompt .
  • The paper highlights that meaningful performance improvements from chain of thought prompts are observed when the prompts are highly specific to their problem class, but these improvements deteriorate as the complexity of the queried problems increases .
  • It extends the evaluation of chain of thought to scalable synthetic benchmarks, including domains like CoinFlip, LastLetterConcatenation, and a synthetic proxy for multi-step arithmetical reasoning, demonstrating the limitations and failure modes of chain of thought prompting .
  • The study emphasizes the drawbacks of chain of thought, particularly the tradeoff between potential performance gains and the significant human labor required to generate examples with correct reasoning traces, shedding light on the challenges of using this method for reasoning tasks .
  • The paper also contributes to the understanding of prompt engineering and in-context learning in LLMs, exploring the capabilities and limitations of these models in reasoning and planning tasks, providing insights into the effectiveness of CoT in classical planning problems .

What work can be continued in depth?

Further research in the field of planning and language models can be expanded by delving deeper into the effectiveness of chain of thought (CoT) approaches. Specifically, exploring how CoT prompts impact the performance of large language models (LLMs) in solving planning problems could be a valuable area of study . Understanding the nuances of how different types of prompts, ranging from specific to general, influence the LLMs' ability to solve problems could provide insights into the underlying mechanisms at play . Additionally, investigating the impact of prompt specificity on the generalization capabilities of LLMs when faced with varying levels of complexity in planning tasks could offer valuable insights into the limitations and strengths of CoT approaches .

Tables

2

Introduction
Background
Overview of Chain of Thought (CoT) prompting
Emergence of CoT in improving LLMs for reasoning tasks
Objective
To evaluate CoT's impact on LLM performance in Blocksworld and synthetic domains
Investigate the limits and trade-offs of CoT with increasing problem complexity
Method
Data Collection
Selection of LLM models for experimentation
Dataset: Blocksworld and other synthetic reasoning tasks
Problem complexity variation
Data Preprocessing
Creation of CoT prompts for different problem types
Tailoring prompts to specific problem instances
Experiment Design
Performance comparison with and without CoT prompts
Control groups with general and problem-specific prompts
Evaluation Metrics
Accuracy, reasoning ability, and generalization across tasks
Results and Analysis
CoT Effectiveness
Performance gains with CoT prompts for simpler problems
Diminishing returns as complexity increases
Algorithm Learning vs. Prompt Dependence
Lack of general algorithm learning
Importance of problem-specific prompt design
Limitations and Trade-offs
Robustness of CoT improvements
Human effort required for prompt creation
Discussion
Implications for LLM performance and prompt engineering
The role of prompt design in enhancing reasoning capabilities
Conclusion
Summary of findings on CoT's effectiveness in reasoning tasks
Future directions for research on prompt optimization and model improvements
Future Work
Exploring alternative prompting techniques for more robust reasoning
Investigating the role of model architecture in CoT performance
Basic info
papers
artificial intelligence
Advanced features
Insights
What does the paper focus on in terms of improving large language models?
What does the research suggest about the general applicability of CoT in LLMs?
In which domains does the study primarily examine the effectiveness of chain of thought prompting?
How do the performance gains from CoT prompting change with increasing problem complexity?

Chain of Thoughtlessness? An Analysis of CoT in Planning

Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati·May 08, 2024

Summary

This paper investigates the effectiveness of chain of thought (CoT) prompting in improving large language models (LLMs) for reasoning tasks, particularly in Blocksworld and other synthetic domains. The study finds that CoT can lead to performance gains when prompts are tailored to specific problems, but these gains are limited and diminish as problem complexity increases. The improvements are not due to the model learning general algorithms but rather rely on problem-specific prompts, highlighting the tradeoff between potential benefits and the need for extensive human effort in creating such prompts. The research suggests that CoT's effectiveness may be less robust than previously thought and questions its general applicability, emphasizing the importance of understanding the limitations and the role of prompt design in LLM performance.
Mind map
Human effort required for prompt creation
Robustness of CoT improvements
Importance of problem-specific prompt design
Lack of general algorithm learning
Diminishing returns as complexity increases
Performance gains with CoT prompts for simpler problems
Accuracy, reasoning ability, and generalization across tasks
Control groups with general and problem-specific prompts
Performance comparison with and without CoT prompts
Tailoring prompts to specific problem instances
Creation of CoT prompts for different problem types
Problem complexity variation
Dataset: Blocksworld and other synthetic reasoning tasks
Selection of LLM models for experimentation
Investigate the limits and trade-offs of CoT with increasing problem complexity
To evaluate CoT's impact on LLM performance in Blocksworld and synthetic domains
Emergence of CoT in improving LLMs for reasoning tasks
Overview of Chain of Thought (CoT) prompting
Investigating the role of model architecture in CoT performance
Exploring alternative prompting techniques for more robust reasoning
Future directions for research on prompt optimization and model improvements
Summary of findings on CoT's effectiveness in reasoning tasks
The role of prompt design in enhancing reasoning capabilities
Implications for LLM performance and prompt engineering
Limitations and Trade-offs
Algorithm Learning vs. Prompt Dependence
CoT Effectiveness
Evaluation Metrics
Experiment Design
Data Preprocessing
Data Collection
Objective
Background
Future Work
Conclusion
Discussion
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Overview of Chain of Thought (CoT) prompting
Emergence of CoT in improving LLMs for reasoning tasks
Objective
To evaluate CoT's impact on LLM performance in Blocksworld and synthetic domains
Investigate the limits and trade-offs of CoT with increasing problem complexity
Method
Data Collection
Selection of LLM models for experimentation
Dataset: Blocksworld and other synthetic reasoning tasks
Problem complexity variation
Data Preprocessing
Creation of CoT prompts for different problem types
Tailoring prompts to specific problem instances
Experiment Design
Performance comparison with and without CoT prompts
Control groups with general and problem-specific prompts
Evaluation Metrics
Accuracy, reasoning ability, and generalization across tasks
Results and Analysis
CoT Effectiveness
Performance gains with CoT prompts for simpler problems
Diminishing returns as complexity increases
Algorithm Learning vs. Prompt Dependence
Lack of general algorithm learning
Importance of problem-specific prompt design
Limitations and Trade-offs
Robustness of CoT improvements
Human effort required for prompt creation
Discussion
Implications for LLM performance and prompt engineering
The role of prompt design in enhancing reasoning capabilities
Conclusion
Summary of findings on CoT's effectiveness in reasoning tasks
Future directions for research on prompt optimization and model improvements
Future Work
Exploring alternative prompting techniques for more robust reasoning
Investigating the role of model architecture in CoT performance
Key findings
5

Paper digest

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

The paper focuses on analyzing the effectiveness of Chain of Thought (CoT) prompts in planning tasks, specifically in the context of Blocksworld problems . The study evaluates the performance of Large Language Models (LLMs) like GPT-4 and Claude-3-Opus when provided with different types of prompts, including zero-shot CoT, domain-specific n-shot, and progression proof CoT . The research aims to understand how well LLMs can apply advice beyond specific instances and how effective they are in problem-solving with the guidance of CoT prompts .

The problem addressed in the paper is not entirely new, as it builds upon previous work on CoT and LLMs in planning and reasoning tasks . The study extends the evaluation of CoT to scalable synthetic benchmarks and considers subsets of Blocksworld problems to assess the generalization capabilities of LLMs with CoT prompts . While the research delves into the nuances of CoT prompting strategies and their impact on problem-solving performance, it does not introduce a fundamentally new problem but rather explores the application of existing techniques in a specific domain .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that Chain-of-Thought (CoT) prompting can enhance reasoning capabilities in large language models, particularly in the context of classical planning problems . The study explores the effectiveness of CoT in enabling models to learn how to reason and plan, with a focus on out-of-domain generalization . The research delves into the relationship between presented chains of thought and the final answers, as well as the impact of prompt specificity on model performance . Additionally, the paper investigates the brittleness and limitations of language models in reasoning and planning tasks, aiming to shed light on the potential of CoT prompting to improve these capabilities .


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

The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" proposes several new ideas, methods, and models related to language models and reasoning abilities . One key concept introduced is the Chain of Thought (CoT), which has become widely adopted for enhancing planning and reasoning skills in Large Language Models (LLMs) . The study questions the effectiveness of operationalizing and generalizing CoT advice in LLMs, suggesting that CoT is only effective when LLMs can perform straightforward pattern matching between examples and problems .

The paper highlights the importance of prompt engineering in improving LLM performance without retraining, emphasizing the capability of LLMs for powerful in-context learning . It discusses the challenges associated with crafting specific prompts that work consistently within a narrow problem class, indicating that very specific prompts may require significant human labor to create but are more likely to be effective .

Furthermore, the study evaluates the Self Consistency extension of CoT on table to stack problems and finds that it does not lead to a generalization breakthrough and may perform worse than the original results in certain scenarios . This analysis sheds light on the limitations of language models in tasks requiring planning and reasoning, cautioning against false confidence in the application of LLMs to such tasks .

Overall, the paper contributes to the understanding of the effectiveness and limitations of CoT methodology in enhancing planning and reasoning abilities of LLMs, emphasizing the need for careful consideration of prompt design and the generalizability of learned procedures across different problem instances . The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" introduces the Chain of Thought (CoT) approach, which aims to enhance planning and reasoning abilities in Large Language Models (LLMs) by providing human-crafted "thoughts" for the LLM to imitate in its response . Compared to previous methods, CoT methodology relies on human labor to provide task-specific knowledge and algorithmic approaches, which can lead to improved performance in complex reasoning tasks . However, the effectiveness of CoT is contingent on the specificity and granularity of the provided knowledge, with very specific prompts showing higher performance but requiring significant human effort to craft .

One key advantage of the CoT approach is its potential to unlock the reasoning abilities of LLMs by teaching them algorithmic procedures through annotated examples, leading to performance enhancements in planning and reasoning tasks . The paper highlights that CoT prompts, when properly constructed, can teach LLMs to generalize basic algorithmic procedures across a large class of problems, thereby converting a modest amount of human teaching effort into a significant capability boost . Additionally, CoT prompts have been shown to induce in-context learning, enabling LLMs to intelligently use additional context provided in prompts to respond correctly to queries .

Furthermore, the study emphasizes the importance of prompt engineering in improving LLM performance without retraining, with CoT being a foundational method for inducing in-context learning . By providing a structured approach to prompt design, CoT prompts aim to guide LLMs through intermediate reasoning steps, allowing them to learn and apply procedures effectively in various problem instances . This method contrasts with previous approaches that may lack systematic scalability or fail to generalize effectively across different problem sets .

In summary, the characteristics and advantages of the Chain of Thought approach, as outlined in the paper, include its reliance on human-crafted "thoughts" to teach LLMs algorithmic procedures, its potential to unlock reasoning abilities and improve performance in planning tasks, and its focus on prompt engineering for in-context learning and generalization across problem instances .


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 studies exist in the field of chain-of-thought reasoning and planning. Noteworthy researchers in this area include Guangsheng Bao, Hongbo Zhang, Linyi Yang, Cunxiang Wang, and Yue Zhang . Additionally, other prominent researchers contributing to this field include Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant . The key to the solution mentioned in the paper involves the utilization of large language models (LLMs) for in-context learning, enabling them to intelligently use additional context provided in a prompt to correctly respond to queries that would otherwise be answered incorrectly .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of GPT-4 and Claude-3-Opus on Blocksworld problems using different prompting methods . The experiments involved testing the models with standard 2-shot prompts and chain of thought prompts of varying granularity, specifically tailored to the intended problem class . The study aimed to assess the impact of chain of thought prompting on problem-solving abilities, particularly in planning domains like Blocksworld, where the difficulty scales with the number of blocks involved . The results indicated that while chain of thought advice can enhance performance on narrow problem distributions, its effectiveness diminishes as the level of specificity increases, highlighting the challenges associated with providing detailed guidance for complex tasks .


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

The dataset used for quantitative evaluation in the study on chain-of-thought in planning tasks is not explicitly mentioned in the provided context . Additionally, there is no information provided regarding the open-source availability of the code used in the research.


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 valuable insights into the effectiveness of chain of thought (CoT) in planning tasks, shedding light on the limitations and challenges associated with this approach . The analysis conducted on the performance of large language models (LLMs) like GPT-4 and Claude-3-Opus in planning domains demonstrates that while CoT may offer some improvements in specific problem distributions, its effectiveness diminishes as the complexity of the problems increases . The study highlights that the ability of LLMs to generalize and reason across different scenarios is limited, especially when faced with tasks requiring multiple reasoning steps or modifications in the problem domain .

Moreover, the research emphasizes the importance of rigorous evaluation mechanisms, urging the scientific community to adopt more comprehensive testing approaches, particularly when assessing the algorithmic reasoning capabilities of black box models like LLMs . The findings suggest that while LLMs may excel in specific problem sets or with basic pattern matching, their performance in handling arbitrary new instances of increasing difficulty remains a challenge . This underscores the need for a deeper understanding of how LLMs learn and apply procedural reasoning, as well as the necessity for robust evaluation frameworks to draw reliable conclusions about their capabilities .

In conclusion, the experiments and results presented in the paper provide valuable insights into the challenges and limitations of employing chain of thought in planning tasks using large language models. The analysis underscores the need for further research to enhance the generalization and reasoning abilities of LLMs, emphasizing the importance of rigorous evaluation methods to ensure the reliability of conclusions drawn about their performance in complex problem-solving scenarios .


What are the contributions of this paper?

The paper "Chain of Thoughtlessness? An Analysis of CoT in Planning" makes several contributions:

  • It presents a case study on the performance of Large Language Models (LLMs) using chain of thought prompting on problems from Blocksworld, a classical planning domain, focusing on the generality of examples given in prompts and the complexity of problems queried with each prompt .
  • The paper highlights that meaningful performance improvements from chain of thought prompts are observed when the prompts are highly specific to their problem class, but these improvements deteriorate as the complexity of the queried problems increases .
  • It extends the evaluation of chain of thought to scalable synthetic benchmarks, including domains like CoinFlip, LastLetterConcatenation, and a synthetic proxy for multi-step arithmetical reasoning, demonstrating the limitations and failure modes of chain of thought prompting .
  • The study emphasizes the drawbacks of chain of thought, particularly the tradeoff between potential performance gains and the significant human labor required to generate examples with correct reasoning traces, shedding light on the challenges of using this method for reasoning tasks .
  • The paper also contributes to the understanding of prompt engineering and in-context learning in LLMs, exploring the capabilities and limitations of these models in reasoning and planning tasks, providing insights into the effectiveness of CoT in classical planning problems .

What work can be continued in depth?

Further research in the field of planning and language models can be expanded by delving deeper into the effectiveness of chain of thought (CoT) approaches. Specifically, exploring how CoT prompts impact the performance of large language models (LLMs) in solving planning problems could be a valuable area of study . Understanding the nuances of how different types of prompts, ranging from specific to general, influence the LLMs' ability to solve problems could provide insights into the underlying mechanisms at play . Additionally, investigating the impact of prompt specificity on the generalization capabilities of LLMs when faced with varying levels of complexity in planning tasks could offer valuable insights into the limitations and strengths of CoT approaches .

Tables
2
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