mlsanityData to 7 Oct 2026

Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation

arXiv 2404.12879 ↗First posted 19 Apr 2024cs.CLcited by 1

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

Abstract (arXiv)

While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law and medicine still suffer from a lack of multi-perspective views, which are essential for improving interpretability and reliability. Previous research on multi-view retrieval often focused solely on different semantic forms of queries, neglecting the expression of specific domain knowledge perspectives. This paper introduces a novel multi-view RAG framework, MVRAG, tailored for knowledge-dense domains that utilizes intention-aware query rewriting from multiple domain viewpoints to enhance retrieval precision, thereby improving the effectiveness of the final inference. Experiments conducted on legal and medical case retrieval demonstrate significant improvements in recall and precision rates with our framework. Our multi-perspective retrieval approach unleashes the potential of multi-view information enhancing RAG tasks, accelerating the further application of LLMs in knowledge-intensive fields.

Key facts. Cited by 1 paper 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 26 of its 35 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

26 of 35 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  2. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov
  3. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 23

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023A Survey of Large Language Modelscited by 2,537
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  4. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  5. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  6. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  7. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  8. 2023Active Retrieval Augmented Generationcited by 506
  9. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  10. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  11. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  12. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
11 more
  1. 2019JEC-QA: A Legal-Domain Question Answering Datasetcited by 115
  2. 2023Towards Open-World Recommendation with Knowledge Augmentation from Large Language Modelscited by 74
  3. 2020Relevance-guided Supervision for OpenQA with ColBERTcited by 71
  4. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  5. 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
  6. 2023LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Datasetcited by 37
  7. 2022Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrievalcited by 26
  8. 2023Retrieving Supporting Evidence for LLMs Generated Answerscited by 21
  9. 2022PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systemscited by 17
  10. 2023Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendationscited by 16
  11. 2023MUSER: A Multi-View Similar Case Retrieval Datasetcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

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  1. 2025RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  3. 2025Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation
  4. 2025Dynamic and Parametric Retrieval-Augmented Generation
  5. 2025RAG-Anything: All-in-One RAG Framework
  6. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
  7. 2025MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework
  8. 2024CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering