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 language | English (US) |
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Title of host publication | Long Papers |
Editors | Lun-Wei Ku, Andre F. T. Martins, Vivek Srikumar |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 11876-11896 |
Number of pages | 21 |
ISBN (Electronic) | 9798891760943 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
Event | 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Bangkok, Thailand Duration: Aug 11 2024 → Aug 16 2024 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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Volume | 1 |
ISSN (Print) | 0736-587X |
Conference
Conference | 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 |
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Country/Territory | Thailand |
City | Bangkok |
Period | 8/11/24 → 8/16/24 |
Bibliographical note
Publisher Copyright:© 2024 Association for Computational Linguistics.