@inproceedings{clymo-etal-2025-ucsc,
    title = "{UCSC} {NLP} T6 at {S}em{E}val-2025 Task 1: Leveraging {LLM}s and {VLM}s for Idiomatic Understanding",
    author = "Clymo, Judith  and
      Zernik, Adam  and
      Gaur, Shubham",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.274/",
    pages = "2103--2115",
    ISBN = "979-8-89176-273-2",
    abstract = "Idiomatic expressions pose a significant challenge for natural language models due to their non-compositional nature. In this work, we address Subtask 1 of the SemEval-2025 Task 1 (ADMIRE), which requires distinguishing between idiomatic and literal usages of phrases and identify images that align with the relevant meaning.Our approach integrates large language models (LLMs) and vision-language models, and we show how different prompting techniques improve those models' ability to identify and explain the meaning of idiomatic language."
}