@inproceedings{spiewak-karas-2025-opi,
    title = "{OPI}-{DRO}-{HEL} at {S}em{E}val-2025 Task 9: Integrating Transformer-Based Classification with {LLM}-Assisted Few-Shot Learning for Food Hazard Detection",
    author = "{\'S}piewak, Martyna  and
      Kara{\'s}, Daniel",
    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-1.155/",
    pages = "1174--1180",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we propose a hybrid approach for food hazard detection that combines a fine-tuned RoBERTa classifier with few-shot learning using an LLM model (GPT-3.5-turbo). We address challenges related to unstructured text and class imbalance by applying class weighting and keyword extraction (KeyBERT, YAKE, and Sentence-BERT). When RoBERTa{'}s confidence falls below a given threshold, a structured prompt which comprising the title, extracted keywords, and a few representative examples is used to re-evaluate the prediction with ChatGPT."
}