DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering

Understand

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.