人类和大模型的词语推断不靠类比，更依赖组合机制。
Is analogy enough to draw novel adjective-noun inferences?
- 用词汇相似度建模类比推理，测试其解释力。
- 多数词组能类比推断，但部分新组合类比失效。
- 类比无法解释所有推断，暗示组合机制存在。
近期研究（Ross等，2025, 2024）认为，人类和大语言模型（LLMs）能对新出现的形容词-名词组合进行泛化推理，表明它们具备确定短语意义的组合性机制。本文探讨这些推理是否仅可通过类比已有推断实现，而无需组合机制。我们通过（1）构建基于词汇项相似性的类比推理模型，（2）让人类参与者进行类比推理实验进行验证。结果显示，该策略在罗斯等（2025）数据集的大部分组合上表现良好，但仍有部分新组合被人类与大模型共同推断出一致结论，却无法通过类比有效处理。因此，我们得出结论：人类与大模型在这些情况下的泛化机制不能完全归结为类比，很可能涉及组合机制。
原文摘要 · Abstract (English)
Recent work (Ross et al., 2025, 2024) has argued that the ability of humans and LLMs respectively to generalize to novel adjective-noun combinations shows that they each have access to a compositional mechanism to determine the phrase's meaning and derive inferences. We study whether these inferences can instead be derived by analogy to known inferences, without need for composition. We investigate this by (1) building a model of analogical reasoning using similarity over lexical items, and (2) asking human participants to reason by analogy. While we find that this strategy works well for a large proportion of the dataset of Ross et al. (2025), there are novel combinations for which both humans and LLMs derive convergent inferences but which are not well handled by analogy. We thus conclude that the mechanism humans and LLMs use to generalize in these cases cannot be fully reduced to analogy, and likely involves composition.
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