Confabulation: The Surprising Value of Large Language Model Hallucinations

Peiqi Sui, Eamon Duede, Sophie Wu, Richard So

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.
Anthology ID:
2024.acl-long.770
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14274–14284
Language:
URL:
https://aclanthology.org/2024.acl-long.770
DOI:
10.18653/v1/2024.acl-long.770
Bibkey:
Cite (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.
Cite (Informal):
Confabulation: The Surprising Value of Large Language Model Hallucinations (Sui et al., ACL 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.acl-long.770.pdf
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