mlsanityData to 7 Oct 2026

Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata

arXiv 2406.13213 ↗First posted 19 Jun 2024cs.CL cs.AI cs.DBcited by 16

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

Abstract (arXiv)

The retrieval-augmented generation (RAG) enables retrieval of relevant information from an external knowledge source and allows large language models (LLMs) to answer queries over previously unseen document collections. However, it was demonstrated that traditional RAG applications perform poorly in answering multi-hop questions, which require retrieving and reasoning over multiple elements of supporting evidence. We introduce a new method called Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata to improve the RAG selection of the relevant documents from various sources, relevant to the question. While database filtering is specific to a set of questions from a particular domain and format, we found out that Multi-Meta-RAG greatly improves the results on the MultiHop-RAG benchmark. The code is available at https://github.com/mxpoliakov/Multi-Meta-RAG.

Key facts. Cited by 16 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 9 of its 15 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

9 of 15 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan

Other references · 8

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 8.

16 papers.

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  1. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systemscited by 22
  2. 2024IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor datacited by 18
  3. 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
  4. 2025Metadata-Driven Retrieval-Augmented Generation for Financial Question Answeringcited by 4
  5. 2025NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answeringcited by 3
  6. 2025Investigating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queriescited by 2
  7. 2026RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systemscited by 2
  8. 2025HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Datacited by 1
  9. 2026Opal: Private Memory for Personal AIcited by 1
  10. 2024E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generationcited by 0
  11. 2026Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Modelscited by 0
  12. 2026SRAG: RAG with Structured Data Improves Vector Retrievalcited by 0
4 more
  1. 2026MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integrationcited by 0
  2. 2026Probe, Don't Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAGcited by 0
  3. 2026EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agentscited by 0
  4. 2026ConRAG: Lightweight inference of multi-hop relationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  2. 2025Question Decomposition for Retrieval-Augmented Generation
  3. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
  4. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documents
  5. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
  6. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  7. 2026Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Survey