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        <title>Confabulation: The Surprising Value of Large Language Model Hallucinations</title>
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    <abstract>This paper presents a systematic defense of large language model (LLM) hallucinations or ‘confabulations’ as a potential resource instead of a categorically negative pitfall. The standard view is that confabulations are inherently problematic and AI research should eliminate this flaw. In this paper, we argue and empirically demonstrate that measurable semantic characteristics of LLM confabulations mirror a human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. In other words, it has potential value. Specifically, we analyze popular hallucination benchmarks and reveal that hallucinated outputs display increased levels of narrativity and semantic coherence relative to veridical outputs. This finding reveals a tension in our usually dismissive understandings of confabulation. It suggests, counter-intuitively, that the tendency for LLMs to confabulate may be intimately associated with a positive capacity for coherent narrative-text generation.</abstract>
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%0 Conference Proceedings
%T Confabulation: The Surprising Value of Large Language Model Hallucinations
%A Sui, Peiqi
%A Duede, Eamon
%A Wu, Sophie
%A So, Richard
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sui-etal-2024-confabulation
%X This paper presents a systematic defense of large language model (LLM) hallucinations or ‘confabulations’ as a potential resource instead of a categorically negative pitfall. The standard view is that confabulations are inherently problematic and AI research should eliminate this flaw. In this paper, we argue and empirically demonstrate that measurable semantic characteristics of LLM confabulations mirror a human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. In other words, it has potential value. Specifically, we analyze popular hallucination benchmarks and reveal that hallucinated outputs display increased levels of narrativity and semantic coherence relative to veridical outputs. This finding reveals a tension in our usually dismissive understandings of confabulation. It suggests, counter-intuitively, that the tendency for LLMs to confabulate may be intimately associated with a positive capacity for coherent narrative-text generation.
%U https://aclanthology.org/2024.acl-long.770
%P 14274-14284

Markdown (Informal)

[Confabulation: The Surprising Value of Large Language Model Hallucinations](https://aclanthology.org/2024.acl-long.770) (Sui et al., ACL 2024)

• Confabulation: The Surprising Value of Large Language Model Hallucinations (Sui et al., ACL 2024)

ACL

• Peiqi Sui, Eamon Duede, Sophie Wu, and Richard So. 2024. Confabulation: The Surprising Value of Large Language Model Hallucinations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14274–14284, Bangkok, Thailand. Association for Computational Linguistics.