@inproceedings{arslan-etal-2025-using,
    title = "Using {LLM}s to Advance Idiom Corpus Construction",
    author = {Arslan, Do{\u{g}}ukan  and
      {\c{C}}akmak, H{\"u}seyin An{\i}l  and
      Eryigit, Gulsen  and
      Nivre, Joakim},
    editor = {Ojha, Atul Kr.  and
      Giouli, Voula  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Korvel, Gra{\v{z}}ina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 21st Workshop on Multiword Expressions (MWE 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.mwe-1.4/",
    pages = "21--31",
    ISBN = "979-8-89176-243-5",
    abstract = "Idiom corpora typically include both idiomatic and literal examples of potentially idiomatic expressions, but creating such corpora traditionally requires substantial expert effort and cost. In this article, we explore the use of large language models (LLMs) to generate synthetic idiom corpora as a more time- and cost-efficient alternative. We evaluate the effectiveness of synthetic data in training task-specific models and testing GPT-4 in few-shot prompting setting using synthetic data for idiomaticity detection. Our findings reveal that although models trained on synthetic data perform worse than those trained on human-generated data, synthetic data generation offers considerable advantages in terms of cost and time. Specifically, task-specific idiomaticity detection models trained on synthetic data outperform the general-purpose LLM that generated the data when evaluated in a zero-shot setting, achieving an average improvement of 11 percentage points across four languages. Moreover, synthetic data enhances the LLM{'}s performance, enabling it to match the task-specific models trained with synthetic data when few-shot prompting is applied."
}