  Retrieval-Augmented Generation (RAG) has recently demonstrated the
performance of Large Language Models (LLMs) in the knowledge-intensive tasks
such as Question-Answering (QA). RAG expands the query context by incorporating
external knowledge bases to enhance the response accuracy. However, it would be
inefficient to access LLMs multiple times for each query and unreliable to
retrieve all the relevant documents by a single query. We have found that even
though there is low relevance between some critical documents and query, it is
possible to retrieve the remaining documents by combining parts of the
documents with the query. To mine the relevance, a two-stage retrieval
framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is
proposed to improve document retrieval recall and the accuracy of answers while
maintaining efficiency. Additionally, a compact classifier is applied to two
different selection strategies to determine the contribution of the retrieved
documents to answering the query and retrieve the relatively relevant
documents. Meanwhile, DR-RAG call the LLMs only once, which significantly
improves the efficiency of the experiment. The experimental results on
multi-hop QA datasets show that DR-RAG can significantly improve the accuracy
of the answers and achieve new progress in QA systems.
