@inproceedings{gargova-etal-2025-comparative,
    title = "A Comparative Study of Hyperbole Detection Methods: From Rule-Based Approaches through Deep Learning Models to Large Language Models",
    author = "Gargova, Silvia  and
      Grigorova, Nevena  and
      Mitkov, Ruslan",
    editor = "Picazo-Izquierdo, Alicia  and
      Estevanell-Valladares, Ernesto Luis  and
      Mitkov, Ruslan  and
      Guillena, Rafael Mu{\~n}oz  and
      Cerd{\'a}, Ra{\'u}l Garc{\'i}a",
    booktitle = "Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.r2lm-1.4/",
    pages = "30--38",
    abstract = "We address hyperbole detection as a binary classification task, comparing rule-based methods, fine-tuned transformers (BERT, RoBERTa), and large language models (LLMs) in zero-shot and few-shot prompting (Gemini, LLaMA). Fine-tuned transformers achieved the best overall performance, with RoBERTa attaining an F1-score of 0.82. Rule-based methods performed lower (F1 = 0.58) but remain effective in constrained linguistic contexts. LLMs showed mixed results: zero-shot performance was variable, while few-shot prompting notably improved outcomes, reaching F1-scores up to 0.79 without task-specific training data. We discuss the trade-offs between interpretability, computational cost, and data requirements across methods. Our results highlight the promise of LLMs in low-resource scenarios and suggest future work on hybrid models and broader figurative language tasks."
}