mlsanityData to 7 Oct 2026

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

arXiv 2404.07220 ↗First posted 22 Mar 2024cs.IR cs.AI cs.CLcited by 83

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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.

Key facts. Cited by 83 papers in the map (data through 7 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.

Builds on

8 of 14 references.

References · 8

  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  5. 2018CoQA: A Conversational Question Answering Challengecited by 674
  6. 2020CORD-19: The COVID-19 Open Research Datasetcited by 179
  7. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  8. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 51; 2026: 16.

83 papers.

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  1. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  2. 2025StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimizationcited by 82
  3. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 65
  4. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  5. 2025HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generationcited by 24
  6. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
  7. 2024When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?cited by 20
  8. 2024FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAGcited by 18
  9. 2024MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaborationcited by 16
  10. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  11. 2024Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systemscited by 12
  12. 2025Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Taskcited by 12
38 more
  1. 2025DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialoguecited by 10
  2. 2025LAG: Logic-Augmented Generation from a Cartesian Perspectivecited by 10
  3. 2024Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphscited by 9
  4. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systemscited by 9
  5. 2025Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systemscited by 9
  6. 2024Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systemscited by 8
  7. 2025DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Roboticscited by 8
  8. 2024Retrieval-Augmented Test Generation: How Far Are We?cited by 7
  9. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspectivecited by 7
  10. 2025DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generationcited by 7
  11. 2025MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrieverscited by 7
  12. 2025ExpReS-VLA: Specializing Vision-Language-Action Models Through Experience Replay and Retrievalcited by 7
  13. 2026Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimizationcited by 7
  14. 2025Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approachcited by 6
  15. 2026Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memorycited by 6
  16. 2025Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysiscited by 5
  17. 2025LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrievalcited by 5
  18. 2025Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapterscited by 5
  19. 2025Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentationscited by 4
  20. 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
  21. 2025Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Searchcited by 4
  22. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generationcited by 4
  23. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answeringcited by 4
  24. 2025Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systemscited by 4
  25. 2026DTCRS: Dynamic Tree Construction for Recursive Summarizationcited by 4
  26. 2025HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning[Technical Report]cited by 3
  27. 2025Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformationcited by 3
  28. 2025SemRAG: Semantic Knowledge-Augmented RAG for Improved Question-Answeringcited by 3
  29. 2025Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RLcited by 3
  30. 2025SemanticCite: Citation Verification with AI-Powered Full-Text Analysis and Evidence-Based Reasoningcited by 3
  31. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarkscited by 3
  32. 2026SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMscited by 3
  33. 2024Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Taskscited by 2
  34. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
  35. 2025Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generationcited by 2
  36. 2025MetaGen Blended RAG: Unlocking Zero-Shot Precision for Specialized Domain Question-Answeringcited by 2
  37. 2025Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoringcited by 2
  38. 2025SINDI: An Efficient Index for Sparse Vector Approximate Maximum Inner Product Searchcited by 2

50 most cited of 83 All 83 (JSON)

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  1. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  2. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  5. 2025QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
  6. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  7. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  8. 2024Semantic Tokens in Retrieval Augmented Generation