Learning to Generate Answers with Citations via Factual Consistency Models

Rami Aly, Zhiqiang Tang, Samson Tan, George Karypis

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

Abstract

Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to relevant sources alongside generated content, enhancing the verifiability of generations. However, citing passages accurately in answers remains a substantial challenge. This paper proposes a weakly-supervised fine-tuning method leveraging factual consistency models (FCMs). Our approach alternates between generating texts with citations and supervised fine-tuning with FCM-filtered citation data. Focused learning is integrated into the objective, directing the fine-tuning process to emphasise the factual unit tokens, as measured by an FCM. Results on the ALCE few-shot citation benchmark with various instruction-tuned LLMs demonstrate superior performance compared to in-context learning, vanilla supervised fine-tuning, and state-of-the-art methods, with an average improvement of 34.1, 15.5, and 10.5 citation F1 points, respectively. Moreover, in a domain transfer setting we show that the obtained citation generation ability robustly transfers to unseen datasets. Notably, our citation improvements contribute to the lowest factual error rate across baselines.

Original languageEnglish (US)
Title of host publicationLong Papers
EditorsLun-Wei Ku, Andre F. T. Martins, Vivek Srikumar
PublisherAssociation for Computational Linguistics (ACL)
Pages11876-11896
Number of pages21
ISBN (Electronic)9798891760943
DOIs
StatePublished - 2024
Externally publishedYes
Event62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Bangkok, Thailand
Duration: Aug 11 2024Aug 16 2024

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume1
ISSN (Print)0736-587X

Conference

Conference62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
Country/TerritoryThailand
CityBangkok
Period8/11/248/16/24

Bibliographical note

Publisher Copyright:
© 2024 Association for Computational Linguistics.

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