UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection

AbstractThis 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.