@inproceedings{li-etal-2025-assessing-llms,
    title = "Assessing {LLM}s' Understanding of Structural Contrasts in the Lexicon",
    author = "Li, Shuxu  and
      Venant, Antoine  and
      Langlais, Philippe  and
      Lareau, Fran{\c{c}}ois",
    editor = "Evang, Kilian  and
      Kallmeyer, Laura  and
      Pogodalla, Sylvain",
    booktitle = "Proceedings of the 16th International Conference on Computational Semantics",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.iwcs-main.9/",
    pages = "98--109",
    ISBN = "979-8-89176-316-6",
    abstract = "We present a new benchmark to evaluate the lexical competence of large language models (LLMs), built on a hierarchical classification of lexical functions (LFs) within the Meaning-Text Theory (MTT) framework. Based on a dataset called French Lexical Network (LN-fr), the benchmark employs contrastive tasks to probe the models' sensitivity to fine-grained paradigmatic and syntagmatic distinctions. Our results show that performance varies significantly across different LFs and systematically declines with increased distinction granularity, highlighting current LLMs' limitations in relational and structured lexical understanding."
}