CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

TL;DR

This paper argues that Chain-of-Thought prompting in large language models does not induce true reasoning but acts as a structural constraint that guides the model to imitate reasoning patterns, leveraging pattern matching and sequence prediction.

Contribution

It provides a theoretical perspective showing CoT as a constraint rather than genuine reasoning, challenging the interpretation of its success.

Findings

CoT guides models to imitate reasoning patterns

CoT enhances performance by constraining output sequences

CoT does not induce true abstract reasoning

Abstract

Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the form of reasoning. By forcing the generation of intermediate steps, Chain-of-Thought leverages the model immense capacity for sequence prediction and pattern matching, effectively constraining its output to sequences that resemble coherent thought processes. Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the form of reasoning. By forcing the generation of intermediate steps, Chain-of-Thought leverages the model immense capacity for sequence prediction and pattern matching, effectively constraining its output to sequences that resemble coherent thought processes.