  Retrieval-augmented generation (RAG) has demonstrated effectiveness in
mitigating the hallucination problem of large language models (LLMs). However,
the difficulty of aligning the retriever with the diverse LLMs' knowledge
preferences inevitably poses an inevitable challenge in developing a reliable
RAG system. To address this issue, we propose DPA-RAG, a universal framework
designed to align diverse knowledge preferences within RAG systems.
Specifically, we initially introduce a preference knowledge construction
pipline and incorporate five novel query augmentation strategies to alleviate
preference data scarcity. Based on preference data, DPA-RAG accomplishes both
external and internal preference alignment: 1) It jointly integrate pair-wise,
point-wise, and contrastive preference alignment abilities into the reranker,
achieving external preference alignment among RAG components. 2) It further
introduces a pre-aligned stage before vanilla Supervised Fine-tuning (SFT),
enabling LLMs to implicitly capture knowledge aligned with their reasoning
preferences, achieving LLMs' internal alignment. Experimental results across
four knowledge-intensive QA datasets demonstrate that DPA-RAG outperforms all
baselines and seamlessly integrates both black-box and open-sourced LLM
readers. Further qualitative analysis and discussions also provide empirical
guidance for achieving reliable RAG systems. Our code is publicly available at
https://github.com/dongguanting/DPA-RAG.
