@inproceedings{anschutz-etal-2025-tum,
    title = "{TUM}-{M}i{K}a{N}i at {S}em{E}val-2025 Task 3: Towards Multilingual and Knowledge-Aware Non-factual Hallucination Identification",
    author = {Ansch{\"u}tz, Miriam  and
      Gikalo, Ekaterina  and
      Herbster, Niklas  and
      Groh, Georg},
    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/corrections-2025-08/2025.semeval-1.141/",
    pages = "1064--1076",
    ISBN = "979-8-89176-273-2",
    abstract = "Hallucinations are one of the major problems of LLMs, hindering their trustworthiness and deployment to wider use cases. However, most of the research on hallucinations focuses on English data, neglecting the multilingual nature of LLMs. This paper describes our submission to the ``{\{}textit{\{}SemEval-2025 Task-3 {---} Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes{\}}{\}}''. We propose a two-part pipeline that combines retrieval-based fact verification against Wikipedia with a BERT-based system fine-tuned to identify common hallucination patterns. Our system achieves competitive results across all languages, reaching top-10 results in eight languages, including English. Moreover, it supports multiple languages beyond the fourteen covered by the shared task. This multilingual hallucination identifier can help to improve LLM outputs and their usefulness in the future."
}