Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the issue of unfaithful chain-of-thought (CoT) reasoning in large language models (LLMs) by proposing the inferential bridging method to mitigate this problem . This problem is not entirely new, as previous works have attempted to measure and explain the unfaithfulness of CoTs but lacked in-depth analysis within CoTs and did not consider the interactions among all reasoning components jointly . The paper delves into the granularity of CoT steps, identifies different reasoning paradigms, and explores the relationship between these paradigms and faithfulness to improve the reasoning process of LLMs .
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 (CoT) reasoning in large language models (LLMs) . The study focuses on addressing the question of whether CoT provides a faithful explanation of the reasoning process in LLMs . The research delves into the granularity of CoT steps, identifies two reasoning paradigms (centralized reasoning and distributed reasoning), and explores their relationship with faithfulness . Additionally, the paper proposes the inferential bridging method to mitigate unfaithfulness issues in CoT by recalling missing information from the context during answer prediction .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners" proposes the following innovative ideas, methods, and models based on the details provided in the document:
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Inferential Bridging Method: The paper introduces an inferential bridging method to address unfaithful chain-of-thought (CoT) issues in large language models (LLMs) . This method aims to fill the information gap in CoTs by recalling correct information from the context during answer prediction . It involves two stages: Inferential Bridging Prompting and Inferential Bridging Filtering .
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Semantic Consistency and Attribution Scores: The proposed method filters out noisy CoTs based on their semantic consistency and attribution scores . By using Natural Language Inference (NLI) Check and AAE Rate modules, incorrect hints that deviate significantly from the question are excluded, and hallucinated statements are rated to retain the highest-scored CoT as the final output .
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Experimental Setup: The paper conducts experiments on datasets like ProofWriter and ProntoQA to evaluate the effectiveness of the proposed method in reasoning scenarios involving multiple steps . Metrics such as accuracy (ACC), ROUGE, and Faithfulness Rate (FR) are used to assess the model's reasoning performance . Ablation studies are also conducted to validate the effectiveness of different modules designed in the method .
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Causal Relevance Analysis: The paper conducts a joint analysis of the causal relevance among the context, CoT, and answer during reasoning . It identifies two reasoning paradigms: centralized reasoning and distributed reasoning, and explores their relationship with faithfulness .
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Interaction Among Reasoning Components: The paper emphasizes the importance of considering the interactions among all reasoning components jointly to improve the faithfulness of LLMs in chain-of-thought reasoning tasks .
Overall, the paper presents a comprehensive approach to mitigate unfaithful CoT issues in large language models by introducing the inferential bridging method, filtering out noisy CoTs, and conducting detailed experimental evaluations to validate the proposed method's effectiveness in enhancing the model's reasoning performance . The proposed inferential bridging method in the paper "Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners" introduces several characteristics and advantages compared to previous methods, as outlined in the document:
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Inferential Bridging Method: The paper introduces an innovative inferential bridging method that addresses unfaithful chain-of-thought (CoT) issues in large language models (LLMs) by recalling correct information from the context during answer prediction . This method consists of two stages: Inferential Bridging Prompting and Inferential Bridging Filtering .
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Semantic Consistency and Attribution Scores: The proposed method filters out noisy CoTs based on their semantic consistency and attribution scores . By utilizing Natural Language Inference (NLI) Check and AAE Rate modules, incorrect hints that deviate significantly from the question are excluded, leading to improved reasoning performance .
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Experimental Evaluation: The paper conducts comprehensive experiments on datasets like ProofWriter and ProntoQA to evaluate the effectiveness of the proposed method in reasoning scenarios involving multiple steps . Metrics such as accuracy (ACC), ROUGE, and Faithfulness Rate (FR) are used to assess the model's reasoning performance, showcasing the superiority of the proposed method over previous approaches .
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Causal Relevance Analysis: The paper conducts a detailed analysis of the causal relevance among the context, CoT, and answer during reasoning, identifying two reasoning paradigms: centralized reasoning and distributed reasoning . This analysis contributes to enhancing the faithfulness of LLMs in chain-of-thought reasoning tasks.
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Interaction Among Reasoning Components: The paper emphasizes the importance of considering the interactions among all reasoning components jointly to improve the faithfulness of LLMs in chain-of-thought reasoning tasks . By integrating various modules and filtering mechanisms, the proposed method enhances the model's reasoning performance and mitigates unfaithful CoT issues effectively.
Overall, the inferential bridging method proposed in the paper stands out due to its focus on semantic consistency, attribution scores, experimental validation, causal relevance analysis, and comprehensive consideration of reasoning components, leading to improved faithfulness in large language models' chain-of-thought reasoning 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 studies have been conducted in the field of faithful chain-of-thought (CoT) reasoning. Noteworthy researchers in this area include Jiachun Li, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao from the School of Artificial Intelligence, University of Chinese Academy of Sciences . Additionally, other researchers such as P. Burkhardt, K. Cobbe, V. Kosaraju, M. Bavarian, H. Jun, L. Kaiser, and many more have contributed to this field .
The key to the solution mentioned in the paper involves the proposal of the inferential bridging method to mitigate the issue of unfaithful CoT reasoning. This method utilizes the attribution method to recall information from the context as hints for CoT generation and filters out noisy CoTs based on their semantic consistency and attribution scores. Extensive experiments have shown that this approach effectively alleviates the problem of unfaithful CoTs .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate the performance of different methods in terms of accuracy, ROUGE score, and faithfulness rate . The experiments involved comparing various methods such as Llama2-13B, Mistral-7B, LtM, and SR across different metrics . The results were presented in tables highlighting the performance of each method in terms of accuracy, ROUGE score, and faithfulness rate . Additionally, ablation experiments were conducted to assess the effectiveness of different modules in enhancing the model's CoT correctness and reasoning faithfulness . The experiments aimed to demonstrate the effectiveness of the proposed inferential bridging method in mitigating unfaithful CoT problems .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the ProofWriter dataset . The code used in the experiments is open source, as indicated by the authors who mentioned that they released all the prompts used in the submitted code .
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 need to be verified. The study conducts comprehensive comparative experiments with various baselines to demonstrate the effectiveness of their method . The results show that their method outperforms other methods in terms of accuracy, ROUGE scores, and faithfulness rates . Additionally, the paper discusses the faithfulness of the Chain-of-Thought (CoT) reasoning and introduces methods to measure and interpret the faithfulness of CoT explanations . These analyses contribute to validating the scientific hypotheses and enhancing the understanding of the effectiveness of the proposed method in bridging reasoning gaps .
What are the contributions of this paper?
The paper "Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners" makes several key contributions:
- It studies the CoT faithfulness issue by analyzing CoT steps, identifying two reasoning paradigms (centralized reasoning and distributed reasoning), and examining their relationship with faithfulness .
- The paper conducts a joint analysis of the causal relevance among the context, CoT, and answer during reasoning, demonstrating how the LLM can recall correct information missing in the CoT from the context, leading to unfaithfulness issues .
- It proposes the inferential bridging method to mitigate unfaithfulness issues by using the attribution method to recall information as hints for CoT generation and filter out noisy CoTs based on semantic consistency and attribution scores .
- The experimental results show that the proposed approach effectively alleviates the unfaithful CoT problem by addressing the issues of noisy hints and unfaithful reasoning processes in large language models .
What work can be continued in depth?
Further work can be continued in depth by delving into the causal relevance among different components of the Chain-of-Thought (CoT) reasoning process. This includes analyzing the interactions among the question context, CoT, and answer jointly to capture the degree of causal relevance between CoTs and answers, which represents the faithfulness of the CoT . Additionally, exploring the granularity of CoT studies to understand that each step in the CoT may play a different role in the reasoning process, rather than treating the CoT as a whole, can provide a more detailed understanding of the model's reasoning process .