@inproceedings{wastl-etal-2025-uzh,
    title = "{UZH} at {S}em{E}val-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection",
    author = "Wastl, Michelle  and
      Vamvas, Jannis  and
      Sennrich, Rico",
    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-1.38/",
    pages = "257--270",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system developed for the SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The objective of this task is to identify spans of hallucinated text in the output of large language models across 14 high- and low- resource languages. To address this challenge, we propose two consistency-based approaches: (a) token-level consistency with a superior LLM and (b) token-level self-consistency with the underlying model of the sequence that is to be evaluated. Our results show effectiveness when compared to simple mark-all baselines, competitiveness to other submissions of the shared task and for some languages to GPT4o- mini prompt-based approaches."
}