@inproceedings{wan-etal-2025-pateam,
    title = "{PAT}eam at {S}em{E}val-2025 Task 9: {LLM}-Augmented Fusion for {AI}-Driven Food Safety Hazard Detection",
    author = "Wan, Xue  and
      Su, Fengping  and
      Sun, Ling  and
      Lin, Yuyang  and
      Chen, Pengfei",
    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-1.249/",
    pages = "1912--1918",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper introduces the approach we adopted for the SemEval-2025 ``Food Hazard Detection'' task, which aims to predict coarse-grained categories (such as ``product category'' and ``hazard category'') and fine-grained vectors (such as specific products like ``ice cream'' or hazards like ``salmonella'') from noisy, long-tailed text data.To address the issues of dirty data, as well as the severe long-tail distribution of text labels and length in the data, we proposed a pipeline system. This system combines data cleaning, LLM-based enhancement, label resampling, and ensemble learning to tackle data sparsity and label imbalance problems.The two subtasks have strong semantic relatedness. By integrating them into a unified multiturn dialogue framework, we fine-tuned five models using a bagging approach. Ultimately, we achieved good results in both subtasks, ranking 5th (with an F1 score of 80.17{\%} for ST1 and 52.66{\%} for ST2)."
}