@inproceedings{pickard-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 1: {A}d{MIR}e - Advancing Multimodal Idiomaticity Representation",
    author = "Pickard, Thomas  and
      Villavicencio, Aline  and
      Mi, Maggie  and
      He, Wei  and
      Phelps, Dylan  and
      Idiart, Marco",
    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/transition-to-people-yaml/2025.semeval-1.330/",
    pages = "2597--2609",
    ISBN = "979-8-89176-273-2",
    abstract = "Idiomatic expressions present a unique challenge in NLP, as their meanings are often notdirectly inferable from their constituent words. Despite recent advancements in Large LanguageModels (LLMs), idiomaticity remains a significant obstacle to robust semantic representation.We present datasets and tasks for SemEval-2025 Task 1: AdMiRe (Advancing Multimodal Idiomaticity Representation), which challenges the community to assess and improve models' ability to interpret idiomatic expressions in multimodal contexts and in multiple languages. Participants competed in two subtasks: ranking images based on their alignment with idiomatic or literal meanings, and predicting the next image in a sequence. The most effective methods achieved human-level performance by leveraging pretrained LLMs and vision-language models in mixture-of-experts settings, with multiple queries used to smooth over the weaknesses in these models' representations of idiomaticity."
}