Abstract
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM’s answer quality. This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM’s information acquisition preferences through reinforcement learning. Evaluations across five datasets show BIDER boosts LLMs’ answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods. The proposed KSE simulation effectively equips LLMs with essential information for accurate question answering.- Anthology ID:
- 2024.findings-acl.42
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 750–761
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.42
- DOI:
- 10.18653/v1/2024.findings-acl.42
- Cite (ACL):
- Jiajie Jin, Yutao Zhu, Yujia Zhou, and Zhicheng Dou. 2024. BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence. In Findings of the Association for Computational Linguistics: ACL 2024, pages 750–761, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence (Jin et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.findings-acl.42.pdf