mlsanityData to 7 Oct 2026

RAG-Fusion: a New Take on Retrieval-Augmented Generation

arXiv 2402.03367 ↗First posted 31 Jan 2024cs.IR cs.LGcited by 42

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

Abstract (arXiv)

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.

Key facts. Cited by 42 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 7 of its 15 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

7 of 15 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  3. 2023A Comprehensive Overview of Large Language Modelscited by 639
  4. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  5. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 49
  6. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 37

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 15; 2026: 18.

42 papers.

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  1. 2024LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentationcited by 28
  2. 2024DMQR-RAG: Diverse Multi-Query Rewriting for RAGcited by 26
  3. 2024Multi-Head RAG: Solving Multi-Aspect Problems with LLMscited by 18
  4. 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
  5. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspectivecited by 7
  6. 2025Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaborationcited by 7
  7. 2024FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipelinecited by 6
  8. 2024HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QAcited by 5
  9. 2025CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writingcited by 5
  10. 2025Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysiscited by 5
  11. 2025syftr: Pareto-Optimal Generative AIcited by 5
  12. 2026Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behaviorcited by 5
30 more
  1. 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
  2. 2025RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasetscited by 4
  3. 2026Taxonomy of the Retrieval System Framework: Pitfalls and Paradigmscited by 4
  4. 2026URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Modelscited by 3
  5. 2024KaPQA: Knowledge-Augmented Product Question-Answeringcited by 2
  6. 2025A Large-Language-Model Framework for Automated Humanitarian Situation Reportingcited by 2
  7. 2024uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?cited by 1
  8. 2024QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyondcited by 1
  9. 2025Tripartite-GraphRAG via Plugin Ontologiescited by 1
  10. 2025Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Stagingcited by 1
  11. 2025GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Modelscited by 1
  12. 2025Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrievalcited by 1
  13. 2025A centroid based framework for text classification in itsm environmentscited by 1
  14. 2026From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documentscited by 1
  15. 2026A Reference Architecture for Agentic Hybrid Retrieval in Dataset Searchcited by 1
  16. 2026Rethinking RAG in Long Videos: What to Retrieve and How to Use It?cited by 1
  17. 2026Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generationcited by 1
  18. 2025Political Events using RAG with LLMscited by 0
  19. 2025AITEE -- Agentic Tutor for Electrical Engineeringcited by 0
  20. 2026The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AIcited by 0
  21. 2026BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detectioncited by 0
  22. 2026RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answeringcited by 0
  23. 2026Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generationscited by 0
  24. 2026Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documentscited by 0
  25. 2026Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Studycited by 0
  26. 2026AstroRAG -- A Pagerank-Based Retrieval-Augmented Generation Pipeline for Question Answering in Astronomycited by 0
  27. 2026Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generationcited by 0
  28. 2026Building AI That Works: ESnet's Pragmatic Approach to AI-Driven Operational Excellencecited by 0
  29. 2026RPTune: Learned Context Curation for LLM Catalog Searchcited by 0
  30. 2026Memory Type Varies: Empowering LLM Agents for Long-Term Memory with Diverse Strategiescited by 0

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  1. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  2. 2024Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework
  3. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  4. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  5. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  6. 2025A survey: Information search time optimization based on RAG (Retrieval Augmentation Generation) chatbot
  7. 2024FACTS About Building Retrieval Augmented Generation-based Chatbots
  8. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions