@inproceedings{lee-yu-2025-refind,
    title = "{REFIND} at {S}em{E}val-2025 Task 3: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models",
    author = "Lee, Donggeon  and
      Yu, Hwanjo",
    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-1.2/",
    pages = "7--15",
    ISBN = "979-8-89176-273-2",
    abstract = "REFIND is a retrieval-augmented framework for detecting hallucinated spans in LLM outputs by leveraging retrieved documents. It introduces Context Sensitivity Ratio, a metric quantifying LLM sensitivity to evidence. REFIND outperforms baselines across nine languages, including low-resource settings, achieving superior hallucination detection accuracy. These results demonstrate the effectiveness of context sensitivity quantification in improving hallucination detection."
}