@inproceedings{bagdasarov-etal-2026-cheese,
    title = "Cheese it up: {C}amem{BERT} Outperforms Large Language Models for Identification of {F}rench Multi-word Expressions",
    author = "Bagdasarov, Sergei  and
      Alves, Diego  and
      Teich, Elke",
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.mwe-1.6/",
    pages = "54--60",
    ISBN = "979-8-89176-363-0",
    abstract = "In recent years, language models, both encoder-only and generative, have been applied to a variety of downstream NLP tasks, includingsequence labeling tasks like automatic multi-word expression identification (MWEI). Multiple studies show that, in general, fine-tunedencoder-only models like BERT tend to outperform pretrained generative LLMs on downstream tasks (Arzideh et al., 2025; Ochoa et al.,2025; Bucher and Martini, 2024; Sebok et al., 2025). However, such comparisons are sparse for MWEI, in particular for French, in partdue to the lack of comprehensive gold-standard datasets. In this study, we address this research gap by comparing CamemBERT with gpt-oss and Qwen3 for MWEI, using the French subcorpus of the newly released PARSEME dataset. CamemBERT outperforms both LLMs by large margins in precision, recall, and F1. We complement this numerical evaluation with a qualitative analysis of prediction errors."
}