@inproceedings{randl-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 9: The Food Hazard Detection Challenge",
    author = "Randl, Korbinian  and
      Pavlopoulos, John  and
      Henriksson, Aron  and
      Lindgren, Tony  and
      Bakagianni, Juli",
    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-1.325/",
    pages = "2523--2534",
    ISBN = "979-8-89176-273-2",
    abstract = "In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we are gradually releasing (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports."
}