Language Models Hallucinate, but May Excel at Fact Verification

Jian Guan, Jesse Dodge, David Wadden, Minlie Huang, Hao Peng

Abstract
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently “hallucinate,” resulting in non-factual outputs. Our carefully-designed human evaluation substantiates the serious hallucination issue, revealing that even GPT-3.5 produces factual outputs less than 25% of the time. This underscores the importance of fact verifiers in order to measure and incentivize progress. Our systematic investigation affirms that LLMs can be repurposed as effective fact verifiers with strong correlations with human judgments. Surprisingly, FLAN-T5-11B , the least factual generator in our study, performs the best as a fact verifier, even outperforming more capable LLMs like GPT3.5 and ChatGPT. Delving deeper, we analyze the reliance of these LLMs on high-quality evidence, as well as their deficiencies in robustness and generalization ability. Our study presents insights for developing trustworthy generation models.
Anthology ID:
2024.naacl-long.62
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1090–1111
Language:
URL:
https://aclanthology.org/2024.naacl-long.62
DOI:
10.18653/v1/2024.naacl-long.62
Bibkey:
Cite (ACL):
Jian Guan, Jesse Dodge, David Wadden, Minlie Huang, and Hao Peng. 2024. Language Models Hallucinate, but May Excel at Fact Verification. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1090–1111, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Language Models Hallucinate, but May Excel at Fact Verification (Guan et al., NAACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.62.pdf
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