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        <title>UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection
%A Wastl, Michelle
%A Vamvas, Jannis
%A Sennrich, Rico
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wastl-etal-2025-uzh
%X 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.
%U https://aclanthology.org/2025.semeval-1.38/
%P 257-270

Markdown (Informal)

[UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection](https://aclanthology.org/2025.semeval-1.38/) (Wastl et al., SemEval 2025)

• UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection (Wastl et al., SemEval 2025)

ACL

• Michelle Wastl, Jannis Vamvas, and Rico Sennrich. 2025. UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 257–270, Vienna, Austria. Association for Computational Linguistics.