Effective Large Language Model Adaptation for Improved Grounding and Citation Generation

Xi Ye, Ruoxi Sun, Sercan Arik, Tomas Pfister


Abstract
Large language models (LLMs) have achieved remarkable advancements in natural language understanding and generation. However, one major issue towards their widespread deployment in the real world is that they can generate “hallucinated” answers that are not factual.Towards this end, this paper focuses on improving LLMs by grounding their responses in retrieved passages and by providing citations. We propose a new framework, AGREE, Adaptation for GRounding EnhancEment, that improves the grounding from a holistic perspective. Our framework tunes LLMs to self-ground the claims in their responses and provide accurate citations to retrieved documents. This tuning on top of the pre-trained LLMs requires well-grounded responses (with citations) for paired queries, for which we introduce a method that can automatically construct such data from unlabeled queries. The self-grounding capability of tuned LLMs further grants them a test-time adaptation (TTA) capability that can actively retrieve passages to support the claims that have not been grounded, which iteratively improves the responses of LLMs. Across five datasets and two LLMs, our results show that the proposed tuning-based framework generates superior grounded responses with more accurate citations compared to prompting-based approaches and post-hoc citing-based approaches.
Anthology ID:
2024.naacl-long.346
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:
6237–6251
Language:
URL:
https://aclanthology.org/2024.naacl-long.346
DOI:
Bibkey:
Cite (ACL):
Xi Ye, Ruoxi Sun, Sercan Arik, and Tomas Pfister. 2024. Effective Large Language Model Adaptation for Improved Grounding and Citation Generation. 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 6237–6251, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation (Ye et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.346.pdf
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