Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers

arXiv:2404.07220v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes increasingly challenging as the corpus of documents scales up, with Retrievers playing an outsized role in the overall RAG accuracy by extracting the most relevant document from the corpus to provide context to the LLM. In this paper, we propose the 'Blended RAG' method of …

abstract accuracy arxiv build cs.ai cs.cl cs.ir documents generative however hybrid improving knowledge knowledge base language language models large language large language models llm query question rag retrieval retrieval-augmented search semantic systems type