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This study introduces a controlled dataset of minimal pairs to probe how language models distinguish between light-verb and full-verb constructions, revealing that models can separate these patterns even in highly constrained contexts. The release of the dataset, generation code, and materials provides a reusable benchmark for evaluating phraseological competence and supports future extensions to broader contexts and additional languages.

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Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models

arXiv4 iunie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.05087v1 Announce Type: new Abstract: 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.

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