CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
This challenges claims about emergent reasoning in LLMs, offering a theoretical perspective for researchers in AI and machine learning, but it is incremental as it critiques existing methods rather than introducing new ones.
The paper tackles the problem of overstating the reasoning capabilities of Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, arguing that CoT does not elicit genuine reasoning but instead acts as a structural constraint that guides LLMs to imitate reasoning forms, without providing concrete performance numbers.
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.