BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
This work addresses a specific bottleneck in retrieval-augmented LLMs for knowledge-intensive tasks like open-domain QA, offering an incremental improvement over existing methods.
The paper tackles the problem of knowledge inconsistency between retrieved documents and the information needed by large language models (LLMs) in retrieval-augmented systems, introducing BIDER to refine documents into key supporting evidence, which boosts LLMs' answer quality by 7% and reduces input length by 80% across five datasets.
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.