@inproceedings{zhang-etal-2025-srcb,
    title = "{SRCB} at {S}em{E}val-2025 Task 9: {LLM} Finetuning Approach based on External Attention Mechanism in The Food Hazard Detection",
    author = "Zhang, Yuming  and
      Li, Hongyu  and
      Zhang, Yongwei  and
      Jiang, Shanshan  and
      Dong, Bin",
    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-1.132/",
    pages = "996--1003",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper reports on the performance of SRCB{'}s system in SemEval-2025 Task 9: The Food Hazard Detection Challenge. We develop a system in the form of a pipeline consisting of two parts: 1. Candidate Recall Module, which selects the most probable correct labels from a large number of labels based on BERT model; 2. LLM Prediction Module, which is used to generate the final prediction based on Large Language Models(LLM). Additionally, to address the issue of long prompts caused by an excessive number of labels, we propose a model architecture to reduce resource consumption and improve performance. Our submission achieves the macro-F1 score of 80.39 on Sub-Task 1 and the macro-F1 score of 54.73 on Sub-Task 2. Our system is released at https://github.com/Doraxgui/Document{\_}Attention"
}