Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy
  with Semantic Search and Hybrid Query-Based Retrievers
Kunal Sawarkar Abhilasha Mangal Shivam Raj Solanki
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 leveraging semantic search techniques,
such as Dense Vector indexes and Sparse Encoder indexes, blended with hybrid
query strategies. Our study achieves better retrieval results and sets new
benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID
datasets. We further extend such a 'Blended Retriever' to the RAG system to
demonstrate far superior results on Generative Q\&A datasets like SQUAD, even
surpassing fine-tuning performance.