用对比句对测试模型对动词用法的分辨能力。
Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models
- 构建最小差异句对数据集，对比同一动词在轻动词与实义动词中的用法。
- 模型能区分动词在不同语境下的语义角色，且对不同宾语类型反应各异。
- 适合研究语言模型句法语义理解能力，尤其关注动词多义性。
常见英语动词如'have'和'make'可作轻动词（如'have a meeting'）或实义动词（如'make a cake'）。当前尚不清楚语言模型是否能区分这种用法差异。本文构建了一个大规模、受控的英文句子系列数据集，其中相同语境下同一动词分别以轻动词和全动词形式出现。两个探针实验表明，语言模型即使在极简上下文中也能区分这两种用法，并在不同宾语类型上表现出可分离的模式。我们公开了数据集、生成代码及相关材料，作为可复用资源。该框架支持扩展至更广上下文、更多动词及其它语言。
原文摘要 · Abstract (English)
Frequent English verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'make a decision' vs. 'make a cake'. Whether language models represent this distinction remains unclear. We introduce a large-scale controlled dataset of minimally varying English sentence series in which the same context contains the same verb in light-verb and full-verb uses. Two probing experiments show that language models differentiate between these uses even in minimal contexts and exhibit separable patterns across object types. We release the dataset, generation code, and materials as a reusable resource. The framework supports extensions to broader contexts, additional verbs, and other languages.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。