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    <abstract>Hallucinations pose a persistent challenge in open-ended question answering (QA). Traditional annotation methods, such as span-labelling, suffer from inconsistency and limited coverage. In this paper, we propose a rewriting-based framework as a new perspective on hallucinations in open-ended QA. We report on an experiment in which annotators are instructed to rewrite LLM-generated answers directly to ensure factual accuracy, with edits automatically recorded. Using the Chinese portion of the Mu-SHROOM dataset, we conduct a controlled rewriting experiment, comparing fact-checking tools (Google vs. GPT-4o), and analysing how tool choice, annotator background, and question openness influence rewriting behaviour. We find that rewriting leads to more hallucinations being identified, with higher inter-annotator agreement, than span-labelling.</abstract>
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%0 Conference Proceedings
%T Annotating Hallucinations in Question-Answering using Rewriting
%A Liu, Xu
%A Chen, Guanyi
%A van Deemter, Kees
%A He, Tingting
%Y Flek, Lucie
%Y Narayan, Shashi
%Y Phương, Lê Hồng
%Y Pei, Jiahuan
%S Proceedings of the 18th International Natural Language Generation Conference
%D 2025
%8 October
%I Association for Computational Linguistics
%C Hanoi, Vietnam
%F liu-etal-2025-annotating
%X Hallucinations pose a persistent challenge in open-ended question answering (QA). Traditional annotation methods, such as span-labelling, suffer from inconsistency and limited coverage. In this paper, we propose a rewriting-based framework as a new perspective on hallucinations in open-ended QA. We report on an experiment in which annotators are instructed to rewrite LLM-generated answers directly to ensure factual accuracy, with edits automatically recorded. Using the Chinese portion of the Mu-SHROOM dataset, we conduct a controlled rewriting experiment, comparing fact-checking tools (Google vs. GPT-4o), and analysing how tool choice, annotator background, and question openness influence rewriting behaviour. We find that rewriting leads to more hallucinations being identified, with higher inter-annotator agreement, than span-labelling.
%U https://aclanthology.org/2025.inlg-main.48/
%P 823-832

Markdown (Informal)

[Annotating Hallucinations in Question-Answering using Rewriting](https://aclanthology.org/2025.inlg-main.48/) (Liu et al., INLG 2025)

• Annotating Hallucinations in Question-Answering using Rewriting (Liu et al., INLG 2025)

ACL

• Xu Liu, Guanyi Chen, Kees van Deemter, and Tingting He. 2025. Annotating Hallucinations in Question-Answering using Rewriting. In Proceedings of the 18th International Natural Language Generation Conference, pages 823–832, Hanoi, Vietnam. Association for Computational Linguistics.