Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners
<|reference_start|>Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners: Large language models (LLMs) suffer from serious unfaithful chain-of-thought (CoT) issues. Previous work attempts to measure and explain it but lacks in-depth analysis within CoTs and does not consider the interactions among all reasoning components jointly. In this paper, we first study the CoT faithfulness issue at the granularity of CoT steps, identify two reasoning paradigms: centralized reasoning and distributed reasoning, and find their relationship with faithfulness. Subsequently, we conduct a joint analysis of the causal relevance among the context, CoT, and answer during reasoning. The result proves that, when the LLM predicts answers, it can recall correct information missing in the CoT from the context, leading to unfaithfulness issues. Finally, we propose the inferential bridging method to mitigate this issue, in which we use the attribution method to recall information as hints for CoT generation and filter out noisy CoTs based on their semantic consistency and attribution scores. Extensive experiments demonstrate that our approach effectively alleviates the unfaithful CoT problem.<|reference_end|>
@article{li2024towards,
    title={Towards Faithful Chain-of-Thought: Large Language Models are Bridging
  Reasoners},
    author={Jiachun Li, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao},
    journal={arXiv preprint arXiv:2405.18915},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18915},
    primaryClass={cs.CL cs.AI}
}