@inproceedings{wang-etal-2025-chain,
    title = "Chain-of-Probe: Examining the Necessity and Accuracy of {C}o{T} Step-by-Step",
    author = "Wang, Zezhong  and
      Zeng, Xingshan  and
      Liu, Weiwen  and
      Wang, Yufei  and
      Li, Liangyou  and
      Wang, Yasheng  and
      Shang, Lifeng  and
      Jiang, Xin  and
      Liu, Qun  and
      Wong, Kam-Fai",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.140/",
    pages = "2586--2606",
    ISBN = "979-8-89176-195-7",
    abstract = "Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phenomenon suggests a potential lack of necessary dependency between the predicted answer and the reasoning process. Consequently, two important questions arise: (1) Is CoT still necessary if the model already has an answer? (2) Can the correctness of the answer serve as valid evidence for the correctness of CoT? To address these questions, we propose a method, namely Chain-of-Probe (CoP), to probe changes in confidence during the model{'}s reasoning. The probing results show that in a significant number of question-answer cases, CoT appears to be unnecessary, and this necessity correlates with the simplicity of the task, defined by the reasoning steps required. Furthermore, by analyzing patterns in confidence change, we examine the correctness of the model{'}s reasoning. Our validation reveals that many responses, although correct in their final answer, contain errors in their reasoning process. To this end, we propose a strategic approach based on CoP to prioritize answers with correct reasoning among multiple candidates, thereby bolstering the reliability of the model{'}s reasoning."
}