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.

介绍了 BIDER，通过知识合成、监督微调和偏好对齐将检索文档转化为关键支持证据（KSE），通过强化学习从 LLM 的信息获取偏好中获得最大化输出，评估结果表明 BIDER 提高了 LLM 的答案质量 7%，同时减少了检索文档中输入内容长度 80%，优于现有方法。该方法的 KSE 模拟有效地装备了 LLM 以获得准确的问题回答。