@inproceedings{lei-etal-2025-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 1: Enhancing Multimodal Idiomaticity Representation via {L}o{RA} and Hybrid Loss Optimization",
    author = "Lei, Liu  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  and
      Zhang, Xuejie",
    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-1.270/",
    pages = "2077--2083",
    ISBN = "979-8-89176-273-2",
    abstract = "This study reports the YNU-HPCC team{'}s participation in Subtask A of SemEval-2025 Task 1 on multimodal idiomatic representation. The task requires ranking candidate images based on their semantic relevance to a target idiom within a given sentence, challenging models to disambiguate idiomatic semantics, and aligning them with abstract visual concepts across English and Portuguese. Using AltCLIP-m18 as the base model, our approach enhances its zero-shot capabilities with LoRA fine-tuning and combines ListMLE ranking optimization with Focal Loss to handle hard samples. Experimental results on the primary test set show significant improvements over the base model, with Top-1 Accuracy/DCG scores of 0.53/2.94 for English and 0.77/3.31 for Portuguese. The code is publicly available at https://github.com/1579364808/Semeval{\_}2025{\_}task1."
}