@inproceedings{tufa-etal-2025-firc-nlp,
    title = "{F}i{RC}-{NLP} at {S}em{E}val-2025 Task 3: Exploring Prompting Approaches for Detecting Hallucinations in {LLM}s",
    author = "Tufa, Wondimagegnhue  and
      Hassan, Fadi  and
      Collell, Guillem  and
      Tu, Dandan  and
      Tu, Yi  and
      Ni, Sang  and
      Tan, Kuan Eeik",
    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-1.273/",
    pages = "2096--2102",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a system description forthe SemEval Mu-SHROOM task, focusing ondetecting hallucination spans in the outputsof instruction-tuned Large Language Models(LLMs) across 14 languages. We comparetwo distinct approaches: Prompt-Based Ap-proach (PBA), which leverages the capabilityof LLMs to detect hallucination spans usingdifferent prompting strategies, and the Fine-Tuning-Based Approach (FBA), which fine-tunes pre-trained Language Models (LMs) toextract hallucination spans in a supervised man-ner. Our experiments reveal that PBA, espe-cially when incorporating explicit references orexternal knowledge, outperforms FBA. How-ever, the effectiveness of PBA varies across lan-guages, likely due to differences in languagerepresentation within LLMs"
}