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    <titleInfo>
        <title>Hallucinated Span Detection with Multi-View Attention Features</title>
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            <namePart type="given">Lea</namePart>
            <namePart type="family">Frermann</namePart>
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    <abstract>This study addresses the problem of hallucinated span detection in the outputs of large language models. It has received less attention than output-level hallucination detection despite its practical importance. Prior work has shown that attentions often exhibit irregular patterns when hallucinations occur. Motivated by these findings, we extract features from the attention matrix that provide complementary views capturing (a) whether certain tokens are influential or ignored, (b) whether attention is biased toward specific subsets, and (c) whether a token is generated referring to a narrow or broad context, in the generation. These features are input to a Transformer-based classifier to conduct sequential labelling to identify hallucinated spans. Experimental results indicate that the proposed method outperforms strong baselines on hallucinated span detection with longer input contexts, such as data-to-text and summarisation tasks.</abstract>
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        <url>https://aclanthology.org/2025.starsem-1.31/</url>
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%0 Conference Proceedings
%T Hallucinated Span Detection with Multi-View Attention Features
%A Ogasa, Yuya
%A Arase, Yuki
%Y Frermann, Lea
%Y Stevenson, Mark
%S Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-340-1
%F ogasa-arase-2025-hallucinated
%X This study addresses the problem of hallucinated span detection in the outputs of large language models. It has received less attention than output-level hallucination detection despite its practical importance. Prior work has shown that attentions often exhibit irregular patterns when hallucinations occur. Motivated by these findings, we extract features from the attention matrix that provide complementary views capturing (a) whether certain tokens are influential or ignored, (b) whether attention is biased toward specific subsets, and (c) whether a token is generated referring to a narrow or broad context, in the generation. These features are input to a Transformer-based classifier to conduct sequential labelling to identify hallucinated spans. Experimental results indicate that the proposed method outperforms strong baselines on hallucinated span detection with longer input contexts, such as data-to-text and summarisation tasks.
%U https://aclanthology.org/2025.starsem-1.31/
%P 381-394

Markdown (Informal)

[Hallucinated Span Detection with Multi-View Attention Features](https://aclanthology.org/2025.starsem-1.31/) (Ogasa & Arase, *SEM 2025)

• Hallucinated Span Detection with Multi-View Attention Features (Ogasa & Arase, *SEM 2025)

ACL

• Yuya Ogasa and Yuki Arase. 2025. Hallucinated Span Detection with Multi-View Attention Features. In Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025), pages 381–394, Suzhou, China. Association for Computational Linguistics.