类比推理之谜：大型语言模型中类比推理机制探究

摘要

类比推理是人类认知的核心，在多种智力活动中发挥着基础性作用。尽管已有研究表明大语言模型能够表征任务模式和表层概念，但这些模型能否编码高层次关系概念并通过结构化比较将其应用于新情境仍不明确。本研究通过比例类比和故事类比探讨这一核心问题，并发现三个关键结论：首先，大语言模型能有效编码类比实体间的底层关系——在正确案例中，属性信息与关系信息共同在中上层传播；而推理失败则反映这些层级中关系信息的缺失。其次，与人类不同，大语言模型不仅因关系信息缺失而受阻，在将关系应用于新实体时也常显吃力，此时在关键标记位置对隐藏表征进行策略性修补可在一定程度上促进信息传递。最后，成功的类比推理以类比情境间的强结构对齐为标志，而失败案例往往表现为结构对齐的弱化或错位。总体而言，我们的研究揭示了大语言模型在编码和应用高层次关系概念时表现出初现但有限的能力，这既凸显了与人类认知的相通之处，也揭示了其存在的差距。

English

Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can represent task patterns and surface-level concepts, it remains unclear whether these models can encode high-level relational concepts and apply them to novel situations through structured comparisons. In this work, we explore this fundamental aspect using proportional and story analogies, and identify three key findings. First, LLMs effectively encode the underlying relationships between analogous entities; both attributive and relational information propagate through mid-upper layers in correct cases, whereas reasoning failures reflect missing relational information within these layers. Second, unlike humans, LLMs often struggle not only when relational information is missing, but also when attempting to apply it to new entities. In such cases, strategically patching hidden representations at critical token positions can facilitate information transfer to a certain extent. Lastly, successful analogical reasoning in LLMs is marked by strong structural alignment between analogous situations, whereas failures often reflect degraded or misplaced alignment. Overall, our findings reveal that LLMs exhibit emerging but limited capabilities in encoding and applying high-level relational concepts, highlighting both parallels and gaps with human cognition.

类比推理之谜：大型语言模型中类比推理机制探究

The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models

摘要

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