@inproceedings{rykov-etal-2025-smurfcat,
    title = "{S}murf{C}at at {S}em{E}val-2025 Task 3: Bridging External Knowledge and Model Uncertainty for Enhanced Hallucination Detection",
    author = "Rykov, Elisei  and
      Olisov, Valerii  and
      Savkin, Maksim  and
      Vazhentsev, Artem  and
      Titova, Kseniia  and
      Panchenko, Alexander  and
      Konovalov, Vasily  and
      Belikova, Julia",
    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-1.137/",
    pages = "1034--1045",
    ISBN = "979-8-89176-273-2",
    abstract = "The Multilingual shared-task on Hallucinations and Related Observable Overgeneration Mistakes in the SemEval-2025 competition aims to detect hallucination spans in the outputs of instruction-tuned LLMs in a multilingual context. In this paper, we address the detection of span hallucinations by applying an ensemble of approaches. In particular, we synthesized a PsiloQA dataset and fine-tuned LLM to detect hallucination spans. In addition, we combined this approach with a white-box method based on uncertainty quantification techniques. Using our combined pipeline, we achieved 3rd place in detecting span hallucinations in Arabic, Catalan, Finnish, Italian, and ranked within the top ten for the rest of the languages."
}