比较人类与大模型对焦点词的语义判断一致性
Focus particles and scalar inferences across humans and language models
- 用约100个句子测试人类和大模型对焦点词的判断
- 两者结果相似但机制可能不同
- 揭示语言模型是否真正理解语义层级
焦点词如"even"和"only"在形式语义学中被认为能构建集合中的替代项结构化表征。"even"强调意外或极端的替代项，而"only"则强制排他性。若此类标量表征具有鲁棒性和泛化性，应在不同语境和系统间产生一致判断。本文使用约100个句子的语料库，测试人类与大语言模型（LLMs）对包含这些焦点词的句子所作的标量判断。初步结果显示，人类与大模型的输出相似，但可能源于不同的底层机制。
原文摘要 · Abstract (English)
Focus particles such as "even" and "only" are central to formal semantic theories that posit structured representations over sets of alternatives. "Even" highlights unexpected or extreme alternatives, while "only" enforces exclusivity. If such scalar representations are robust and generalizable, they should give rise to consistent judgments across contexts and systems. In this work, we test whether humans and large language models (LLMs) construct stable scalar representations from sentences containing these particles. Using a dataset of approximately 100 items, participants and models were asked to make scalar judgments. Preliminary results suggest that similar outputs across humans and LLMs may arise from different underlying mechanisms.
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