Language Models Hallucinate, but May Excel at Fact Verification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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-T511B, 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.

Original languageEnglish (US)
Title of host publicationLong Papers
EditorsKevin Duh, Helena Gomez, Steven Bethard
PublisherAssociation for Computational Linguistics (ACL)
Pages1090-1111
Number of pages22
ISBN (Electronic)9798891761148
DOIs
StatePublished - 2024
Event2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024 - Hybrid, Mexico City, Mexico
Duration: Jun 16 2024Jun 21 2024

Publication series

NameProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024
Volume1

Conference

Conference2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024
Country/TerritoryMexico
CityHybrid, Mexico City
Period6/16/246/21/24

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Software

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