Dissociation of Faithful and Unfaithful Reasoning in LLMs

Evelyn Yee, Alice Li, Chenyu Tang, Yeon Ho Jung, Ramamohan Paturi, Leon Bergen·May 23, 2024

Summary

This preprint investigates the faithfulness of large language models (LLMs) in their Chain of Thought (CoT) reasoning process, using the dissociation paradigm from psychology. The study distinguishes between faithful (correcting errors) and unfaithful (incoherent or superficial) recoveries. LLMs, like GPT-3.5 and GPT-4, are found to recover more often from obvious errors and with supportive context, while unfaithful recoveries are more common in challenging situations. The research highlights that error recovery does not always indicate coherent reasoning, suggesting distinct mechanisms for faithful and unfaithful thinking. Experiments manipulate error types, magnitude, and context to analyze these behaviors, with GPT-4 generally showing better recovery rates. The study challenges the assumption of a uniform reasoning process in LLMs and calls for further investigation into their cognitive processes and potential biases.

Paper digest

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

The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" aims to investigate how Large Language Models (LLMs) recover from errors in Chain of Thought reasoning text to reach the correct final answer despite mistakes in the reasoning text . This research delves into error recovery behaviors in LLMs, identifying instances of unfaithfulness in Chain of Thought, while also highlighting examples of faithful error recovery behaviors . The study explores factors influencing LLM recovery behavior, indicating that LLMs recover more frequently from obvious errors and in contexts providing more evidence for the correct answer, but unfaithful recoveries occur more frequently for more challenging error positions . While the paper addresses the issue of error recovery in LLMs, it also delves into the distinction between faithful and unfaithful error recoveries, shedding light on the mechanisms driving these distinct behaviors . This problem of investigating error recovery behaviors in LLMs and distinguishing between faithful and unfaithful recoveries is a new area of research within the context of Large Language Models .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the faithfulness of chain of thought reasoning in language models . The study investigates the ability of language models to recover from errors in their chain of thought texts and measures the impact on faithful and unfaithful reasoning . The research delves into the concept of faithfulness in chain of thought, distinguishing between "plausible" and "faithful" explanations . It explores how language models can unfaithfully rationalize answers based on superficial cues in the prompt and the disconnect between the model's generated reasoning text and its final answer . The study also utilizes counterfactual interventions to assess the significance of tokens in the model's reasoning text and examines the potential mediating factor of task instructions in the alignment between chain of thought text and the model's final answer .


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

The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" proposes several new ideas, methods, and models related to chain-of-thought reasoning in large language models (LLMs) . Here are some key points from the paper:

  1. Chain-of-Thought Reasoning: The paper explores the concept of chain-of-thought reasoning in LLMs, focusing on the faithfulness and unfaithfulness of the models in generating reasoning chains .

  2. Evaluation Methods: It introduces methodologies for evaluating the faithfulness of chain-of-thought reasoning in LLMs, including the use of prompts, responses, and ground-truth data from math word problem datasets .

  3. Error Recovery Rates: The paper discusses error recovery rates for GPT-4 with textual adjustments, providing insights into the model's performance in recovering from errors .

  4. Model Querying Pipeline: It describes a 2-pass querying pipeline for evaluating chain-of-thought reasoning, involving providing questions, chain of thought prompts, and extracting numerical answers from the models .

  5. Perturbations in Chain of Thought: The paper details how numerical errors are introduced into the chain of thought text to assess the model's response to errors, including copying errors, calculation errors, and propagated calculation errors .

  6. Multimodal Infillings: It explores the concept of visual chain of thought, aiming to bridge logical gaps in reasoning chains using multimodal infillings .

  7. Unfaithful Behavior: The research highlights instances of unfaithful behavior in chain-of-thought reasoning by LLMs, distinguishing between plausible and faithful explanations and calling for further development of faithful systems .

  8. Theoretical Perspectives: The paper provides theoretical perspectives on error propagation in model-generated text, the disconnect between chain of thought text and final answers, and the role of task instructions in model reasoning .

Overall, the paper contributes to the understanding of chain-of-thought reasoning in LLMs, emphasizing the importance of faithfulness, evaluation methods, error recovery, and theoretical insights into model behavior. The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" introduces novel methods and insights in the realm of chain-of-thought reasoning in large language models (LLMs) . Here are the characteristics and advantages compared to previous methods:

  1. Evaluation Methods: The paper proposes new evaluation methodologies to assess the faithfulness of chain-of-thought reasoning in LLMs, including the use of prompts, responses, and ground-truth data from math word problem datasets . This approach enhances the understanding of model reasoning behavior and performance.

  2. Error Recovery Rates: It delves into error recovery rates for GPT-4 with textual adjustments, shedding light on the model's ability to recover from errors, both faithful and unfaithful, with a focus on calculation errors and propagated calculation errors . This analysis provides insights into the model's robustness and error-handling capabilities.

  3. Distinct Mechanisms: The research identifies distinct mechanisms underlying faithful and unfaithful error reasoning in LLMs, highlighting how different behaviors emerge in the recovery process . This distinction contributes to a deeper understanding of error recovery dynamics in model-generated reasoning chains.

  4. Prior Expectations Experiment: The paper conducts Experiment 3 to evaluate the impact of prior expectations on error recovery rates by introducing noise into the transcript or directly prompting the model with error information . This experiment demonstrates how manipulating prior expectations can influence the model's recovery behavior.

  5. Multimodal Infillings: The study by Himakunthala et al. (2023) on visual chain of thought is referenced, emphasizing the importance of multimodal infillings in bridging logical gaps in reasoning chains . This approach enriches the interpretability and coherence of reasoning processes in LLMs.

  6. Related Work Insights: The paper contextualizes its contributions within related work, such as investigations into chain-of-thought generalizability, error categorizations, and reasoning errors in LLMs . By building on existing research, the paper advances the understanding of chain-of-thought reasoning and error recovery mechanisms.

Overall, the paper's innovative methods, experimental findings, and theoretical insights contribute significantly to the field of LLM reasoning, offering a nuanced understanding of faithful and unfaithful reasoning behaviors, error recovery dynamics, and the impact of prior expectations on model performance.


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 faithful and unfaithful reasoning in large language models (LLMs). Noteworthy researchers in this area include Leo Gao, Konstantin Hebenstreit, Robert Praas, Louis P Kiesewetter, Matthias Samwald, Alon Jacovi, Yoav Goldberg, Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, Hannaneh Hajishirzi, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, and many others .

The key to the solution mentioned in the paper involves understanding the faithfulness of error recovery behaviors in large language models. The research focuses on annotating error responses to identify whether the model successfully recovers from errors and whether the error recovery behavior is faithful or unfaithful. This annotation process helps evaluate the model's ability to recover from errors and maintain faithfulness in its reasoning process .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate error recovery behaviors in language models. The experiments involved manipulating the perceptibility of errors by changing their magnitude . Errors with greater magnitude were expected to be more noticeable to the model, resulting in higher rates of recovery . The study used four math word problem datasets: MultiArith, ASDiv, SVAMP, and GSM8K, and evaluated each model on all available questions in the test set . For each model in each dataset, 300 <question, chain of thought, answer> triples were randomly sampled, where the model achieved the correct answer, forming the ground-truth data for further experiments . The experiments also involved introducing numerical errors using regular expressions and manually verifying the errors introduced were of the correct type and essential to the logic of the problem solution . Additionally, the experiments included faithfulness annotation, where each error response was manually annotated to identify whether the model recovered from the error and whether the error recovery behavior was faithful or unfaithful .


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

The datasets used for quantitative evaluation in the study are MultiArith, ASDiv, SVAMP, and GSM8K . The code and data for the experiments, along with instructions for reproducing the results, will be made available at the GitHub repository: https://github.com/CoTErrorRecovery/CoTErrorRecovery . However, it is important to note that OpenAI has announced that access to the GPT-3.5 and GPT-4 checkpoints evaluated in the study may be permanently deprecated as early as June 2024 .


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 strong support for the scientific hypotheses that needed verification. The study conducted experiments to investigate the behavior of faithful and unfaithful recoveries in large language models (LLMs) like GPT-4 . The results demonstrated a clear dissociation in the behavior of faithful and unfaithful recoveries, showing that a larger amount of evidence for the correct value increased the rate of faithful recoveries significantly, but had a smaller effect on unfaithful recoveries . This finding aligns with the hypothesis that different factors influence faithful and unfaithful recoveries differently.

Furthermore, the experiments manipulated the perceptibility of errors by changing their magnitude, with errors of greater magnitude expected to be more noticeable to the model . The results showed that errors with larger magnitudes led to higher rates of faithful recovery than errors with smaller magnitudes, indicating that error magnitude plays a crucial role in the recovery process . This experimental setup effectively tested the hypothesis regarding the impact of error magnitude on error recovery rates.

Moreover, the study utilized multinomial logistic regression with fixed effects for datasets to estimate the effects of different variables on error recovery . By analyzing the numerical results across different datasets, error positions, and error amounts, the study provided a comprehensive evaluation of the factors influencing faithful and unfaithful recoveries in LLMs like GPT-4 . This analytical approach supported the scientific hypotheses by providing detailed insights into the behavior of these models under varying conditions.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses related to faithful and unfaithful reasoning in LLMs. The methodology employed, the manipulation of variables, and the thorough analysis of results contribute to a comprehensive understanding of the behavior of these models in error recovery scenarios, validating the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper makes several contributions:

  • It focuses on measuring faithfulness in chain-of-thought reasoning .
  • It discusses sources of hallucination by large language models on inference tasks .
  • It evaluates and develops English math word problem solvers using a diverse corpus .
  • It directly evaluates chain-of-thought in multi-hop reasoning with knowledge graphs .
  • It explores the concept of visual chain of thought for bridging logical gaps with multimodal infillings .
  • It provides insights into the error recovery rates for GPT-4 with textual adjustments .
  • It uses various datasets for evaluation, including MultiArith, ASDiv, SVAMP, and GSM8K, to analyze model responses .
  • It delves into the behavior of large language models as zero-shot reasoners .
  • It discusses the Shapley value attribution in the chain of thought and the generalization of prompts to novel models and datasets .
  • It contributes to the understanding of how to define and evaluate faithfulness in NLP systems .

What work can be continued in depth?

Further research can be continued in depth on the topic of faithfulness in chain of thought reasoning in large language models (LLMs). This includes investigating the recovery from errors in chain of thought texts and analyzing the factors that influence the model's ability to reach the correct final answer despite mistakes in the reasoning text . Additionally, exploring instances of unfaithful behavior in chain of thought and distinguishing between "plausible" and "faithful" explanations can be areas of focus for future studies . Understanding the mechanisms that drive faithful and unfaithful error recoveries in LLMs can provide valuable insights into the reasoning processes of these models .


Introduction
Background
Overview of large language models (LLMs) and Chain of Thought (CoT) reasoning
Importance of understanding CoT in AI development
Objective
To investigate the faithfulness of LLMs in CoT reasoning
Examine the distinction between faithful and unfaithful error recoveries
Challenge uniform reasoning assumption in LLMs
Method
Data Collection
LLM Models
Selection of models: GPT-3.5 and GPT-4
Dataset generation: CoT reasoning prompts with varying error types, magnitudes, and contexts
Error Manipulation
Error types: Obvious vs. challenging errors
Error magnitude: Degree of deviation from correct reasoning
Contextual influence: Supportive vs. ambiguous or conflicting context
Experiment Design
Procedure for assessing error recovery and reasoning coherence
Use of dissociation paradigm from psychology
Data Analysis
Quantitative analysis of recovery rates
Qualitative analysis of faithful vs. unfaithful recoveries
Results
Faithful Reasoning
Conditions for correct error correction
Evidence of supportive context enhancing faithful recovery
Unfaithful Reasoning
Incidence of incoherent or superficial recoveries
Examples of unfaithful thinking in challenging situations
Comparison between GPT-3.5 and GPT-4
Performance differences in error recovery
Discussion
Interpretation of findings in terms of cognitive mechanisms
Implications for model biases and uniformity of reasoning
Limitations and future research directions
Conclusion
Summary of key findings
The need for further investigation into LLMs' cognitive processes
Implications for the development of more transparent and reliable AI systems
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What conclusion does the study draw about the reasoning process of LLMs and the need for further investigation?
What is the primary focus of the preprint regarding large language models?
How does the dissociation paradigm from psychology play a role in the study?
In what situations do LLMs, like GPT-3.5 and GPT-4, tend to exhibit unfaithful recoveries?

Dissociation of Faithful and Unfaithful Reasoning in LLMs

Evelyn Yee, Alice Li, Chenyu Tang, Yeon Ho Jung, Ramamohan Paturi, Leon Bergen·May 23, 2024

Summary

This preprint investigates the faithfulness of large language models (LLMs) in their Chain of Thought (CoT) reasoning process, using the dissociation paradigm from psychology. The study distinguishes between faithful (correcting errors) and unfaithful (incoherent or superficial) recoveries. LLMs, like GPT-3.5 and GPT-4, are found to recover more often from obvious errors and with supportive context, while unfaithful recoveries are more common in challenging situations. The research highlights that error recovery does not always indicate coherent reasoning, suggesting distinct mechanisms for faithful and unfaithful thinking. Experiments manipulate error types, magnitude, and context to analyze these behaviors, with GPT-4 generally showing better recovery rates. The study challenges the assumption of a uniform reasoning process in LLMs and calls for further investigation into their cognitive processes and potential biases.
Mind map
Dataset generation: CoT reasoning prompts with varying error types, magnitudes, and contexts
Selection of models: GPT-3.5 and GPT-4
Performance differences in error recovery
Examples of unfaithful thinking in challenging situations
Incidence of incoherent or superficial recoveries
Evidence of supportive context enhancing faithful recovery
Conditions for correct error correction
Qualitative analysis of faithful vs. unfaithful recoveries
Quantitative analysis of recovery rates
Use of dissociation paradigm from psychology
Procedure for assessing error recovery and reasoning coherence
Contextual influence: Supportive vs. ambiguous or conflicting context
Error magnitude: Degree of deviation from correct reasoning
Error types: Obvious vs. challenging errors
LLM Models
Challenge uniform reasoning assumption in LLMs
Examine the distinction between faithful and unfaithful error recoveries
To investigate the faithfulness of LLMs in CoT reasoning
Importance of understanding CoT in AI development
Overview of large language models (LLMs) and Chain of Thought (CoT) reasoning
Implications for the development of more transparent and reliable AI systems
The need for further investigation into LLMs' cognitive processes
Summary of key findings
Limitations and future research directions
Implications for model biases and uniformity of reasoning
Interpretation of findings in terms of cognitive mechanisms
Comparison between GPT-3.5 and GPT-4
Unfaithful Reasoning
Faithful Reasoning
Data Analysis
Experiment Design
Error Manipulation
Data Collection
Objective
Background
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Overview of large language models (LLMs) and Chain of Thought (CoT) reasoning
Importance of understanding CoT in AI development
Objective
To investigate the faithfulness of LLMs in CoT reasoning
Examine the distinction between faithful and unfaithful error recoveries
Challenge uniform reasoning assumption in LLMs
Method
Data Collection
LLM Models
Selection of models: GPT-3.5 and GPT-4
Dataset generation: CoT reasoning prompts with varying error types, magnitudes, and contexts
Error Manipulation
Error types: Obvious vs. challenging errors
Error magnitude: Degree of deviation from correct reasoning
Contextual influence: Supportive vs. ambiguous or conflicting context
Experiment Design
Procedure for assessing error recovery and reasoning coherence
Use of dissociation paradigm from psychology
Data Analysis
Quantitative analysis of recovery rates
Qualitative analysis of faithful vs. unfaithful recoveries
Results
Faithful Reasoning
Conditions for correct error correction
Evidence of supportive context enhancing faithful recovery
Unfaithful Reasoning
Incidence of incoherent or superficial recoveries
Examples of unfaithful thinking in challenging situations
Comparison between GPT-3.5 and GPT-4
Performance differences in error recovery
Discussion
Interpretation of findings in terms of cognitive mechanisms
Implications for model biases and uniformity of reasoning
Limitations and future research directions
Conclusion
Summary of key findings
The need for further investigation into LLMs' cognitive processes
Implications for the development of more transparent and reliable AI systems

Paper digest

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

The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" aims to investigate how Large Language Models (LLMs) recover from errors in Chain of Thought reasoning text to reach the correct final answer despite mistakes in the reasoning text . This research delves into error recovery behaviors in LLMs, identifying instances of unfaithfulness in Chain of Thought, while also highlighting examples of faithful error recovery behaviors . The study explores factors influencing LLM recovery behavior, indicating that LLMs recover more frequently from obvious errors and in contexts providing more evidence for the correct answer, but unfaithful recoveries occur more frequently for more challenging error positions . While the paper addresses the issue of error recovery in LLMs, it also delves into the distinction between faithful and unfaithful error recoveries, shedding light on the mechanisms driving these distinct behaviors . This problem of investigating error recovery behaviors in LLMs and distinguishing between faithful and unfaithful recoveries is a new area of research within the context of Large Language Models .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the faithfulness of chain of thought reasoning in language models . The study investigates the ability of language models to recover from errors in their chain of thought texts and measures the impact on faithful and unfaithful reasoning . The research delves into the concept of faithfulness in chain of thought, distinguishing between "plausible" and "faithful" explanations . It explores how language models can unfaithfully rationalize answers based on superficial cues in the prompt and the disconnect between the model's generated reasoning text and its final answer . The study also utilizes counterfactual interventions to assess the significance of tokens in the model's reasoning text and examines the potential mediating factor of task instructions in the alignment between chain of thought text and the model's final answer .


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

The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" proposes several new ideas, methods, and models related to chain-of-thought reasoning in large language models (LLMs) . Here are some key points from the paper:

  1. Chain-of-Thought Reasoning: The paper explores the concept of chain-of-thought reasoning in LLMs, focusing on the faithfulness and unfaithfulness of the models in generating reasoning chains .

  2. Evaluation Methods: It introduces methodologies for evaluating the faithfulness of chain-of-thought reasoning in LLMs, including the use of prompts, responses, and ground-truth data from math word problem datasets .

  3. Error Recovery Rates: The paper discusses error recovery rates for GPT-4 with textual adjustments, providing insights into the model's performance in recovering from errors .

  4. Model Querying Pipeline: It describes a 2-pass querying pipeline for evaluating chain-of-thought reasoning, involving providing questions, chain of thought prompts, and extracting numerical answers from the models .

  5. Perturbations in Chain of Thought: The paper details how numerical errors are introduced into the chain of thought text to assess the model's response to errors, including copying errors, calculation errors, and propagated calculation errors .

  6. Multimodal Infillings: It explores the concept of visual chain of thought, aiming to bridge logical gaps in reasoning chains using multimodal infillings .

  7. Unfaithful Behavior: The research highlights instances of unfaithful behavior in chain-of-thought reasoning by LLMs, distinguishing between plausible and faithful explanations and calling for further development of faithful systems .

  8. Theoretical Perspectives: The paper provides theoretical perspectives on error propagation in model-generated text, the disconnect between chain of thought text and final answers, and the role of task instructions in model reasoning .

Overall, the paper contributes to the understanding of chain-of-thought reasoning in LLMs, emphasizing the importance of faithfulness, evaluation methods, error recovery, and theoretical insights into model behavior. The paper "Dissociation of Faithful and Unfaithful Reasoning in LLMs" introduces novel methods and insights in the realm of chain-of-thought reasoning in large language models (LLMs) . Here are the characteristics and advantages compared to previous methods:

  1. Evaluation Methods: The paper proposes new evaluation methodologies to assess the faithfulness of chain-of-thought reasoning in LLMs, including the use of prompts, responses, and ground-truth data from math word problem datasets . This approach enhances the understanding of model reasoning behavior and performance.

  2. Error Recovery Rates: It delves into error recovery rates for GPT-4 with textual adjustments, shedding light on the model's ability to recover from errors, both faithful and unfaithful, with a focus on calculation errors and propagated calculation errors . This analysis provides insights into the model's robustness and error-handling capabilities.

  3. Distinct Mechanisms: The research identifies distinct mechanisms underlying faithful and unfaithful error reasoning in LLMs, highlighting how different behaviors emerge in the recovery process . This distinction contributes to a deeper understanding of error recovery dynamics in model-generated reasoning chains.

  4. Prior Expectations Experiment: The paper conducts Experiment 3 to evaluate the impact of prior expectations on error recovery rates by introducing noise into the transcript or directly prompting the model with error information . This experiment demonstrates how manipulating prior expectations can influence the model's recovery behavior.

  5. Multimodal Infillings: The study by Himakunthala et al. (2023) on visual chain of thought is referenced, emphasizing the importance of multimodal infillings in bridging logical gaps in reasoning chains . This approach enriches the interpretability and coherence of reasoning processes in LLMs.

  6. Related Work Insights: The paper contextualizes its contributions within related work, such as investigations into chain-of-thought generalizability, error categorizations, and reasoning errors in LLMs . By building on existing research, the paper advances the understanding of chain-of-thought reasoning and error recovery mechanisms.

Overall, the paper's innovative methods, experimental findings, and theoretical insights contribute significantly to the field of LLM reasoning, offering a nuanced understanding of faithful and unfaithful reasoning behaviors, error recovery dynamics, and the impact of prior expectations on model performance.


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 faithful and unfaithful reasoning in large language models (LLMs). Noteworthy researchers in this area include Leo Gao, Konstantin Hebenstreit, Robert Praas, Louis P Kiesewetter, Matthias Samwald, Alon Jacovi, Yoav Goldberg, Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, Hannaneh Hajishirzi, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, and many others .

The key to the solution mentioned in the paper involves understanding the faithfulness of error recovery behaviors in large language models. The research focuses on annotating error responses to identify whether the model successfully recovers from errors and whether the error recovery behavior is faithful or unfaithful. This annotation process helps evaluate the model's ability to recover from errors and maintain faithfulness in its reasoning process .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate error recovery behaviors in language models. The experiments involved manipulating the perceptibility of errors by changing their magnitude . Errors with greater magnitude were expected to be more noticeable to the model, resulting in higher rates of recovery . The study used four math word problem datasets: MultiArith, ASDiv, SVAMP, and GSM8K, and evaluated each model on all available questions in the test set . For each model in each dataset, 300 <question, chain of thought, answer> triples were randomly sampled, where the model achieved the correct answer, forming the ground-truth data for further experiments . The experiments also involved introducing numerical errors using regular expressions and manually verifying the errors introduced were of the correct type and essential to the logic of the problem solution . Additionally, the experiments included faithfulness annotation, where each error response was manually annotated to identify whether the model recovered from the error and whether the error recovery behavior was faithful or unfaithful .


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

The datasets used for quantitative evaluation in the study are MultiArith, ASDiv, SVAMP, and GSM8K . The code and data for the experiments, along with instructions for reproducing the results, will be made available at the GitHub repository: https://github.com/CoTErrorRecovery/CoTErrorRecovery . However, it is important to note that OpenAI has announced that access to the GPT-3.5 and GPT-4 checkpoints evaluated in the study may be permanently deprecated as early as June 2024 .


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 strong support for the scientific hypotheses that needed verification. The study conducted experiments to investigate the behavior of faithful and unfaithful recoveries in large language models (LLMs) like GPT-4 . The results demonstrated a clear dissociation in the behavior of faithful and unfaithful recoveries, showing that a larger amount of evidence for the correct value increased the rate of faithful recoveries significantly, but had a smaller effect on unfaithful recoveries . This finding aligns with the hypothesis that different factors influence faithful and unfaithful recoveries differently.

Furthermore, the experiments manipulated the perceptibility of errors by changing their magnitude, with errors of greater magnitude expected to be more noticeable to the model . The results showed that errors with larger magnitudes led to higher rates of faithful recovery than errors with smaller magnitudes, indicating that error magnitude plays a crucial role in the recovery process . This experimental setup effectively tested the hypothesis regarding the impact of error magnitude on error recovery rates.

Moreover, the study utilized multinomial logistic regression with fixed effects for datasets to estimate the effects of different variables on error recovery . By analyzing the numerical results across different datasets, error positions, and error amounts, the study provided a comprehensive evaluation of the factors influencing faithful and unfaithful recoveries in LLMs like GPT-4 . This analytical approach supported the scientific hypotheses by providing detailed insights into the behavior of these models under varying conditions.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses related to faithful and unfaithful reasoning in LLMs. The methodology employed, the manipulation of variables, and the thorough analysis of results contribute to a comprehensive understanding of the behavior of these models in error recovery scenarios, validating the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper makes several contributions:

  • It focuses on measuring faithfulness in chain-of-thought reasoning .
  • It discusses sources of hallucination by large language models on inference tasks .
  • It evaluates and develops English math word problem solvers using a diverse corpus .
  • It directly evaluates chain-of-thought in multi-hop reasoning with knowledge graphs .
  • It explores the concept of visual chain of thought for bridging logical gaps with multimodal infillings .
  • It provides insights into the error recovery rates for GPT-4 with textual adjustments .
  • It uses various datasets for evaluation, including MultiArith, ASDiv, SVAMP, and GSM8K, to analyze model responses .
  • It delves into the behavior of large language models as zero-shot reasoners .
  • It discusses the Shapley value attribution in the chain of thought and the generalization of prompts to novel models and datasets .
  • It contributes to the understanding of how to define and evaluate faithfulness in NLP systems .

What work can be continued in depth?

Further research can be continued in depth on the topic of faithfulness in chain of thought reasoning in large language models (LLMs). This includes investigating the recovery from errors in chain of thought texts and analyzing the factors that influence the model's ability to reach the correct final answer despite mistakes in the reasoning text . Additionally, exploring instances of unfaithful behavior in chain of thought and distinguishing between "plausible" and "faithful" explanations can be areas of focus for future studies . Understanding the mechanisms that drive faithful and unfaithful error recoveries in LLMs can provide valuable insights into the reasoning processes of these models .

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