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.

当前研究发现了大型语言模型中的早期回答问题，即模型在生成思维链之前已经有了答案，这表明预测答案和推理过程之间可能存在必要的依赖缺失。为了解决这些问题，我们提出了一种名为 Chain-of-Probe（CoP）的方法，用于探究模型推理过程中的思维变化。探测结果显示，在许多问题 - 答案案例中，Chain-of-Thought 似乎是不必要的，这种必要性与任务的简单性相关，由所需的推理步骤定义。此外，通过分析思维变化中的模式，我们检验了模型推理的正确性。验证结果显示，虽然最终答案是正确的，但很多回答在推理过程中存在错误。为此，我们提出了一种基于 CoP 的策略方法，以优先考虑具有正确推理的答案，从而增强模型推理的可靠性。