大模型看似推理，实则靠模式生成，本质是随机文本续写。
What Kind of Reasoning (if any) is an LLM actually doing? On the Stochastic Nature and Abductive Appearance of Large Language Models
- 基于训练数据中的模式进行文本生成，非真正推理
- 输出看似合理解释，但无真实语义与验证能力
- 适合辅助创意，但需严格审核其结论可靠性
本文探讨当前基于令牌续写的大型语言模型（LLMs）如何实现推理。分析表明，这些模型的输出源于对人类生成文本中推理结构的学习，而非真正的类比推理（abductive reasoning）。尽管模型能生成看似合理的推论、模仿常识推理并提供解释性回答，但其内容缺乏真实性、语义基础、可验证性与理解能力。这种兼具随机性与类比表象的双重特性，对模型评估与应用具有重要意义：它们可辅助构思与思维拓展，但其输出必须被批判性审查，因模型无法识别真理或验证自身解释。文章回应了五项质疑，指出分析局限，并给出总体评价。
原文摘要 · Abstract (English)
This article looks at how reasoning works in current Large Language Models (LLMs) that function using the token-completion method. It examines their stochastic nature and their similarity to human abductive reasoning. The argument is that these LLMs create text based on learned patterns rather than performing actual abductive reasoning. When their output seems abductive, this is largely because they are trained on human-generated texts that include reasoning structures. Examples are used to show how LLMs can produce plausible ideas, mimic commonsense reasoning, and give explanatory answers without being grounded in truth, semantics, verification, or understanding, and without performing any real abductive reasoning. This dual nature, where the models have a stochastic base but appear abductive in use, has important consequences for how LLMs are evaluated and applied. They can assist with generating ideas and supporting human thinking, but their outputs must be critically assessed because they cannot identify truth or verify their explanations. The article concludes by addressing five objections to these points, noting some limitations in the analysis, and offering an overall evaluation.
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