Hallucinated Span Detection with Multi-View Attention Features

ACL ARR 2025 May Submission2031 Authors

18 May 2025 (modified: 03 Jul 2025)ACL ARR 2025 May SubmissionEveryoneRevisionsBibTeXCC BY 4.0
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.
Paper Type: Long
Research Area: Language Modeling
Research Area Keywords: safety and alignment,text-to-text generation,automatic evaluation,misinformation detection
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 2031
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