用语言模型研究通用句的隐含量化，发现其高度依赖语境且多为弱普遍性。
Generics are puzzling. Can language models find the missing piece?
- 构建上下文通用句数据集ConGen，分析语言模型对通用句的理解机制。
- 20%的通用句表达弱普遍性，且比限定词更依赖具体语境。
- 揭示语言模型中人类刻板印象的潜在偏见，适合关注自然语言语义的研究者。
通用句在不显式量化的情况下表达对世界的普遍性判断。尽管在日常交流中至关重要，但构建精确的语义框架仍具挑战，部分原因在于说话者使用通用句概括具有不同统计普遍性的属性。本文通过语言模型作为语言表征工具，研究通用句的隐含量化与语境敏感性。我们构建了包含2873个自然发生通用句与量化句的ConGen数据集，并提出基于意外度（surprisal）的p-acceptability指标，用于捕捉量化敏感性。实验表明，通用句的语境敏感性高于限定词，且约20%的自然通用句表达的是弱普遍性。此外，我们还探讨了人类刻板印象如何在语言模型中体现。
原文摘要 · Abstract (English)
Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, a metric based on surprisal that is sensitive to quantification. Our experiments show generics are more context-sensitive than determiner quantifiers and about 20% of naturally occurring generics we analyze express weak generalisations. We also explore how human biases in stereotypes can be observed in language models.
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