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        <title>Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models</title>
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    <abstract>Frequent 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, and whether such representations vary across languages, remains unclear. We introduce a large-scale controlled dataset in English, Spanish, and French, comprising minimally varying 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.</abstract>
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%0 Conference Proceedings
%T Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models
%A Franzon, Francesca
%A Gómez, Nicolás Rosàs
%A Wanner, Leo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F franzon-etal-2026-light
%X Frequent 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, and whether such representations vary across languages, remains unclear. We introduce a large-scale controlled dataset in English, Spanish, and French, comprising minimally varying 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.
%U https://aclanthology.org/2026.findings-emnlp.969/
%P 20499-20512

Markdown (Informal)

[Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models](https://aclanthology.org/2026.findings-emnlp.969/) (Franzon et al., Findings 2026)

• Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models (Franzon et al., Findings 2026)

ACL

• Francesca Franzon, Nicolás Rosàs Gómez, and Leo Wanner. 2026. Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20499–20512, Budapest, Hungary. Association for Computational Linguistics.