@inproceedings{yu-etal-2025-tuecl,
    title = "{T}ue{CL} at {S}em{E}val-2025 Task 1: Image-Augmented Prompting and Multimodal Reasoning for Enhanced Idiom Understanding",
    author = "Yu, Yue  and
      Tang, Jiarong  and
      Liu, Ruitong",
    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-1.230/",
    pages = "1753--1758",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our approach for SemEval-2025 Task 1, Advancing Multimodal Idiomaticity Representation (AdMIRe), which focuses on idiom image ranking via semantic similarity. We explore multiple strategies, including neural networks on extracted embeddings and Siamese networks with triplet loss. A key component of our methodology is the application of advanced prompt engineeringtechniques within multimodal in-context learning (ManyICL), leveraging GPT-4o, CLIP.Our experiments demonstrate that structured and optimized prompts significantly enhancethe model{'}s ability to interpret idiomatic expressions in a multimodal setting."
}