CoT并非真正的推理，而只是一种用于模仿的严格约束：理论视角
CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
AI总结：
本文从理论角度反驳CoT激发大模型真实推理能力的观点，指出其本质是通过中间步骤约束输出，使模型模仿推理形式。
摘要：
Chain-of-Thought（CoT，思维链）提示已被证明能够提升大型语言模型在需要多步推理的任务上的表现。这一成功使人们广泛声称这些模型涌现出了推理能力。本文提出一种理论上的反视角：Chain-of-Thought（CoT）并不会引发真正的、抽象的推理。相反，我们认为思维链充当了一种强有力的结构性约束，引导大型语言模型模仿推理的形式。通过强制生成中间步骤，思维链利用了模型在序列预测和模式匹配方面的强大能力，有效地将其输出约束为类似于连贯思维过程的序列。Chain-of-Thought（CoT）提示已被证明能够提升大型语言模型在需要多步推理的任务上的表现。这一成功使人们广泛声称这些模型涌现出了推理能力。本文提出一种理论上的反视角：Chain-of-Thought（CoT）并不会引发真正的、抽象的推理。相反，我们认为思维链充当了一种强有力的结构性约束，引导大型语言模型模仿推理的形式。通过强制生成中间步骤，思维链利用了模型在序列预测和模式匹配方面的强大能力，有效地将其输出约束为类似于连贯思维过程的序列。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
