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

arXiv: https://arxiv.org/abs/2606.05087 · HTML: https://mlsanity.com/paper/2606.05087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [CLiMP: A Benchmark for Chinese Language Model Evaluation](https://mlsanity.com/paper/2101.11131/) (2021)
- [A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans](https://mlsanity.com/paper/2106.03232/) (2021)
- [Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models](https://mlsanity.com/paper/2105.03495/) (2021)
- [Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representations](https://mlsanity.com/paper/2601.03779/) (2026)

## Built on by (most cited first, 1 of 1)

- [Contextual Embedding Evidence for Main--Light Verb Distinctions in Urdu](https://mlsanity.com/paper/2608.23645/) (2026)

## Abstract (arXiv)

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.