  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by
incorporating additional information from retrieval. However, studies have
shown that LLMs still face challenges in effectively using the retrieved
information, even ignoring it or being misled by it. The key reason is that the
training of LLMs does not clearly make LLMs learn how to utilize input
retrieved texts with varied quality. In this paper, we propose a novel
perspective that considers the role of LLMs in RAG as ``Information Refiner'',
which means that regardless of correctness, completeness, or usefulness of
retrieved texts, LLMs can consistently integrate knowledge within the retrieved
texts and model parameters to generate the texts that are more concise,
accurate, and complete than the retrieved texts. To this end, we propose an
information refinement training method named InFO-RAG that optimizes LLMs for
RAG in an unsupervised manner. InFO-RAG is low-cost and general across various
tasks. Extensive experiments on zero-shot prediction of 11 datasets in diverse
tasks including Question Answering, Slot-Filling, Language Modeling, Dialogue,
and Code Generation show that InFO-RAG improves the performance of LLaMA2 by an
average of 9.39\% relative points. InFO-RAG also shows advantages in in-context
learning and robustness of RAG.
