  Retrieval augmented generation (RAG) has been applied in many scenarios to
augment large language models (LLMs) with external documents provided by
retrievers. However, a semantic gap exists between LLMs and retrievers due to
differences in their training objectives and architectures. This misalignment
forces LLMs to passively accept the documents provided by the retrievers,
leading to incomprehension in the generation process, where the LLMs are
burdened with the task of distinguishing these documents using their inherent
knowledge. This paper proposes R$^2$AG, a novel enhanced RAG framework to fill
this gap by incorporating Retrieval information into Retrieval Augmented
Generation. Specifically, R$^2$AG utilizes the nuanced features from the
retrievers and employs a R$^2$-Former to capture retrieval information. Then, a
retrieval-aware prompting strategy is designed to integrate retrieval
information into LLMs' generation. Notably, R$^2$AG suits low-source scenarios
where LLMs and retrievers are frozen. Extensive experiments across five
datasets validate the effectiveness, robustness, and efficiency of R$^2$AG. Our
analysis reveals that retrieval information serves as an anchor to aid LLMs in
the generation process, thereby filling the semantic gap.
