UZH at S em E val-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection (2025)

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.

## introduction large language models (llms) have transcended the boundaries of the natural language processing community and hold significant potential for automating tasks across different industries. however, their adoption is often hindered by concerns regarding their trustworthiness and factual reliability, particularly due to hallucinations -the phenomenon where generative systems produce incorrect or fabricated output. thus, developing methods to detect hallucinations is an essential step towards ensuring the safe and effective deployment of llms in real-world applications. various approaches have been undertaken that showed the effectiveness of detecting hallucinations through retrieval augmentation and referring to external knowledge @xcite @xcite or leveraging the internal states of the llm @xcite @xcite . these methods are restricted by the fact that they rely on access to external knowledge and the openness of the llm. a further method to detect hallucination that is not impacted by these factors leverages the self-and cross-model consistency for a given task for the same or across different llms @xcite . while effective, these methods have been almost exclusively tested at the sentence level, however, hallucinations often occur at the sub-sentential level, with incorrect or fabricated information appearing within specific spans of text rather than entire sentences. less work has gone into detecting hallucinations at a more fine-grained level @xcite @xcite . this year's semeval task 3 mushroom: the multilingual shared-task on hallucinations and related observable overgeneration mistakes @xcite calls for research to fill this gap, by challenging the community on a hallucination span detection task, providing a human-annotated dataset of question-answer pairs, in which the answers have been generated by various open-source language models. the dataset contains labelled data for 10 languages (german, english, french, italian, spanish, hindi, chinese, arabic, swedish, and finnish) and unlabelled data for 4 additional languages (farsi, basque, czech, and catalan) -bringing multilingual and low-resource components to the challenge. we approach this challenge by adapting self-and cross-model consistency approaches to the token level. our proposed approach compares alternative responses by the same underlying model that has been used to generate the answer that is to be evaluated as a way of self-consistency or by a superior llm by aligning each token of the answer that is to be evaluated with a token from each alternative answer as a way of cross-model consistency (gptconsistency). the median similarity score between the aligned tokens is then used as an indicator for token-level consistency, or hallucinations. we find that simply prompting a strong llm results in a strong baseline, reaching the highest results on average across languages with an overlap with gold annotations of up to 51.7%, consistencybased approaches can outperform it or achieve comparable results for languages like chinese, french, finnish or swedish, reaching overlaps with human gold annotations of 47.9%-60.1% ## background this work takes inspiration in selfconsistency @xcite , a framework in which chain-of-thought (cot) prompting is improved by sampling more than one possible continuation and choosing the continuation that is generated most consistently. self-consistency has also been adapted to the hallucination detection task under the assumption that inconsistency is an indicator for counter-factuality and model uncertainty. it is proven effective at the sentence-level hallucination detection task in english: @xcite compare an llm-generated response against stochastically-generated responses and let an llm judge over the factuality of the response. @xcite investigate self-contradiction as an indicator for hallucinations. @xcite introduce the concept of cross-model consistency, building on the observation that one model can hallucinate consistently across multiple continuations and therefore, an additional llm is needed to break the consistency. in this work, we continue to follow the intuition that inconsistency indicates model uncertainty and that, especially in a cross-model setting with a superior llm, inconsistency can be used for hallucination detection at sub-sentential level. ## conclusion in this work, we introduced a (self-)consistencybased approach to hallucination span detection in llm-generated responses for multilingual question-answering as part of the submission for the semeval 2025 shared task 3: mu-shroom, the multilingual shared-task on hallucinations and related observable overgeneration mistakes. our method leverages both token-level selfconsistency and cross-model consistency with a superior llm (gpt4o-mini) to identify hallucinated spans. we demonstrated that our methods outperform naive baselines and remain comparable with state-of-the-art approaches for certain languages, even when operating under limited resource constraints. the results indicate that self-consistency is highly dependent on model-and language-specific threshold calibration and that it is most effective when applied to responses generated by larger models (>8b parameters), where it oftentimes outperforms the gpt-based zero-shot baseline. future work could improve the stability across smaller models by combining self-consistency and gptconsistency, or extend consistency-based methods to additional generative tasks beyond questionanswering.