Papers › Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic...
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | SQuAD1.1 dev | Blended RAG | EM | 57.63 | #2 of 4 | Archive leaderboard | report |
| Question Answering | NQ (BEIR) | Blended RAG | nDCG@10 | 0.67 | #1 of 6 | Archive leaderboard | report |
| Question Answering | Natural Questions | Blended RAG | EM | 42.63 | #21 of 47 | Archive leaderboard | report |
| Question Answering | SQuAD | Blended RAG | Exact Match | 57.63 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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