Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step
This addresses reliability concerns in LLM reasoning for AI safety and interpretability, but it is incremental as it builds on existing CoT research.
The paper tackles the issue of Early Answering in LLMs, where models have answers before generating Chain-of-Thought, questioning the necessity and accuracy of CoT; it finds that CoT is often unnecessary, correlating with task simplicity, and many correct answers contain reasoning errors, proposing a method to prioritize answers with correct reasoning.
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.