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.

翻译：本文探讨当前基于词元补全机制运行的大型语言模型（LLMs）的推理运作方式，分析其随机性本质及其与人类溯因推理的相似性。核心论点为：这些LLMs基于习得模式生成文本，而非执行真正的溯因推理。当模型输出呈现溯因特征时，主要归因于其训练数据源自包含推理结构的人类文本。通过示例展示LLMs如何生成看似合理的观点、模仿常识推理并提供解释性答案，而无需基于事实、语义、验证或理解，亦未执行任何真实的溯因推理。这种随机基础与溯因表象并存的二元特性，对LLMs的评估与应用具有重要影响：它们可辅助创意生成并支持人类思维，但其输出必须经过严格审慎评估，因其无法辨识真伪或验证自身解释。文章最后针对五点质疑作出回应，指出分析中的若干局限，并给出整体评价。