@inproceedings{s-etal-2025-madhans476,
    title = "madhans476 at {S}em{E}val-2025 Task 9: Multi-Model Ensemble and Prompt-Based Learning for Food Hazard Prediction",
    author = "S, Madhan  and
      R, Gnanesh  and
      D, Gopal  and
      Saumya, Sunil",
    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-1.88/",
    pages = "627--633",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a hybrid approach to food hazard detection for SemEval-2025 Task 9, combining traditional machine learning with advanced language models. For hazard classification (Sub-Task 1), we implemented a novel ensemble system integrating XGBoost with fine-tuned GPT-2 Large and LLaMA 3.1 1B models. For vector detection (Sub-Task 2), we employed a prompt-engineered approach using Flan-T5-XL, highlighting challenges in exact vector matching. Our analysis demonstrates the effectiveness of combining complementary models while revealing opportunities for improvement in rare category detection and extraction precision."
}