@inproceedings{heerema-etal-2025-raggedyfive,
    title = "{R}aggedy{F}ive at {S}em{E}val-2025 Task 3: Hallucination Span Detection Using Unverifiable Answer Detection",
    author = "Heerema, Wessel  and
      Krooneman, Collin  and
      Van Loon, Simon  and
      Top, Jelmer  and
      Voors, Maurice",
    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-1.194/",
    pages = "1473--1478",
    ISBN = "979-8-89176-273-2",
    abstract = "Despite their broad utility, large language models (LLMs) are prone to hallucinations. The deviation from provided source inputs or disparateness with factual accuracy makes users question the reliability of LLMs. Therefore, detection systems for LLMs on hallucination are imperative. The system described in this paper detects hallucinated text spans by combining Retrieval-Augmented Generation (RAG) with Natural Language Interface (NLI). While zero-context handling of the RAG had little measurable effect, incorporating the RAG into a natural-language premise for the NLI yielded a noticeable improvement. Discrepancies can be attributed to labeling methodology and the implementation of the RAG."
}