研究事件真假与隐喻表达的关系，发现人类更擅长区分。
Contextualising (Im)plausible Events Triggers Figurative Language
- 构建真假事件对，结合抽象/具体成分测试
- 人类能精准识别非字面意义事件，大模型则倾向误判
- 适合语言理解、认知计算领域研究者阅读
本研究以英语主谓宾事件为例，探讨非字面性与事件合理性之间的关系。通过系统设计合理与不合理事件三元组，并结合抽象与具体成分类别，分析人类与大语言模型生成的判断及例句上下文。结果表明，人类在辨别非字面与不合理事件方面具有精细的语境感知能力，而大语言模型仅表现出浅层语境关联，存在将不合理事件误判为非字面合理解释的倾向。
原文摘要 · Abstract (English)
This work explores the connection between (non-)literalness and plausibility at the example of subject-verb-object events in English. We design a systematic setup of plausible and implausible event triples in combination with abstract and concrete constituent categories. Our analysis of human and LLM-generated judgments and example contexts reveals substantial differences between assessments of plausibility. While humans excel at nuanced detection and contextualization of (non-)literal vs. implausible events, LLM results reveal only shallow contextualization patterns with a bias to trade implausibility for non-literal, plausible interpretations.
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