Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step

TL;DR

This paper investigates the necessity of Chain-of-Thought reasoning in large language models, revealing many correct answers lack proper reasoning and proposing a probing method to improve reasoning reliability.

Contribution

It introduces Chain-of-Probe (CoP), a novel method to analyze and enhance the necessity and correctness of reasoning steps in LLMs' answers.

Findings

Many correct answers lack proper reasoning.

CoT is unnecessary for simple tasks with fewer reasoning steps.

Using CoP can prioritize answers with correct reasoning.

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 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.