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        <title>Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts</title>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures.</abstract>
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%0 Conference Proceedings
%T Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts
%A Cha, Taehun
%A Lee, Donghun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F cha-lee-2024-pre
%X In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures.
%U https://aclanthology.org/2024.findings-emnlp.738
%P 12630-12639

Markdown (Informal)

[Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts](https://aclanthology.org/2024.findings-emnlp.738) (Cha & Lee, Findings 2024)

• Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts (Cha & Lee, Findings 2024)

ACL

• Taehun Cha and Donghun Lee. 2024. Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12630–12639, Miami, Florida, USA. Association for Computational Linguistics.