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

arXiv: https://arxiv.org/abs/2404.07220 · HTML: https://mlsanity.com/paper/2404.07220/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 8 of 8)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)

## Built on by (most cited first, 30 of 84)

- [StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization](https://mlsanity.com/paper/2505.15107/) (2025)
- [Searching for Best Practices in Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01219/) (2024)
- [Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely](https://mlsanity.com/paper/2409.14924/) (2024)
- [A Survey on Knowledge-Oriented Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10677/) (2025)
- [When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?](https://mlsanity.com/paper/2410.15267/) (2024)
- [HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.12442/) (2025)
- [Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases](https://mlsanity.com/paper/2410.14594/) (2024)
- [FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG](https://mlsanity.com/paper/2410.10293/) (2024)
- [MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration](https://mlsanity.com/paper/2410.12532/) (2024)
- [Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation](https://mlsanity.com/paper/2411.03572/) (2024)
- [Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems](https://mlsanity.com/paper/2407.08275/) (2024)
- [Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task](https://mlsanity.com/paper/2504.03616/) (2025)
- [DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue](https://mlsanity.com/paper/2502.13847/) (2025)
- [LAG: Logic-Augmented Generation from a Cartesian Perspective](https://mlsanity.com/paper/2508.05509/) (2025)
- [Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs](https://mlsanity.com/paper/2412.07618/) (2024)
- [Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2503.15191/) (2025)
- [Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems](https://mlsanity.com/paper/2511.01268/) (2025)
- [Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization](https://mlsanity.com/paper/2604.14267/) (2026)
- [Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems](https://mlsanity.com/paper/2411.19710/) (2024)
- [DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Robotics](https://mlsanity.com/paper/2507.04661/) (2025)
- [Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memory](https://mlsanity.com/paper/2603.16862/) (2026)
- [Retrieval-Augmented Test Generation: How Far Are We?](https://mlsanity.com/paper/2409.12682/) (2024)
- [Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective](https://mlsanity.com/paper/2410.12812/) (2024)
- [DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.23013/) (2025)
- [MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers](https://mlsanity.com/paper/2506.15862/) (2025)
- [ExpReS-VLA: Specializing Vision-Language-Action Models Through Experience Replay and Retrieval](https://mlsanity.com/paper/2511.06202/) (2025)
- [Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://mlsanity.com/paper/2502.14100/) (2025)
- [Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapters](https://mlsanity.com/paper/2511.17044/) (2025)
- [Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations](https://mlsanity.com/paper/2504.04771/) (2025)
- [Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2504.05324/) (2025)

## Abstract (arXiv)

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.