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 the mind 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 reasoning steps required. Furthermore, by analyzing patterns in mind 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.
Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Tianjin University（天津大学）；City University of Hong Kong（香港城市大学）；University College London（伦敦大学学院）；Peking University（北京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of Bristol（布里斯托大学）
Measuring Chain-of-Thought Monitorability Through Faithfulness and Verbosity
ML Alignment and Theory Scholars（机器学习对齐与理论学者计划）；University of Delaware（特拉华大学）；AI Safety Argentina（阿根廷AI安全组织）；Buenos Aires AI Safety Hub（布宜诺斯艾利斯AI安全中心）；University of Buenos Aires（布宜诺斯艾利斯大学）；Center for AI Safety（AI安全中心）
Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization
University of Copenhagen（哥本哈根大学）；Technische Universität Berlin（柏林技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD – 柏林学习与数据基础研究院）
Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；Didichuxing Co. Ltd（滴滴出行有限公司）；CSE, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
