  Large Language Models (LLMs) have demonstrated superior results across a wide
range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to
enhance the performance by locating relevant information and placing it into
the context window of the LLM. However, the relationship between retrievers and
LLMs in a RAG is still under-investigated. Most existing work treats the
retriever and the LLM as independent components and leaves a gap between
retrieving human-"friendly" information and assembling a LLM-"friendly"
context. In this work, we examine a novel bridge mechanism. We validate the
ranking and selection assumptions of retrievers in the context of RAG and
propose a framework that chains together supervised and reinforcement learning
to train a bridge model that optimizes the connection between the retriever and
the LLM. Empirical results demonstrate the effectiveness of our method in both
question-answering and personalized generation tasks.
