@inproceedings{hashiloni-etal-2025-easy,
    title = "Easy as {PIE}? Identifying Multi-Word Expressions with {LLM}s",
    author = "Hashiloni, Kai Golan  and
      Hefetz, Ofri  and
      Bar, Kfir",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1213/",
    pages = "23782--23801",
    ISBN = "979-8-89176-332-6",
    abstract = "We investigate the identification of idiomatic expressions{---}a semantically non-compositional subclass of multiword expressions (MWEs){---}in running text using large language models (LLMs) without any fine-tuning. Instead, we adopt a prompt-based approach and evaluate a range of prompting strategies, including zero-shot, few-shot, and chain-of-thought variants, across multiple languages, datasets, and model types. Our experiments show that, with well-crafted prompts, LLMs can perform competitively with supervised models trained on annotated data. These findings highlight the potential of prompt-based LLMs as a flexible and effective alternative for idiomatic expression identification."
}