mlsanityData to 14 Oct 2026

HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases

arXiv 2412.16311 ↗First posted 20 Dec 2024cs.LG cs.AI cs.IRcited by 29

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source. However, many questions require both textual and relational information from SKB - referred to as "hybrid" questions - which complicates the retrieval process and underscores the need for a hybrid retrieval method that leverages both information. In this paper, through our empirical analysis, we identify key insights that show why existing methods may struggle with hybrid question answering (HQA) over SKB. Based on these insights, we propose HybGRAG for HQA consisting of a retriever bank and a critic module, with the following advantages: (1) Agentic, it automatically refines the output by incorporating feedback from the critic module, (2) Adaptive, it solves hybrid questions requiring both textual and relational information with the retriever bank, (3) Interpretable, it justifies decision making with intuitive refinement path, and (4) Effective, it surpasses all baselines on HQA benchmarks. In experiments on the STaRK benchmark, HybGRAG achieves significant performance gains, with an average relative improvement in Hit@1 of 51%.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 23 of its 33 references that are in the map. First posted on arXiv 20 Dec 2024.

Builds on

23 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  7. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  9. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  10. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  11. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  12. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
10 more
  1. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  2. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  3. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  4. 2023REFINER: Reasoning Feedback on Intermediate Representationscited by 205
  5. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  6. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 119
  7. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approachcited by 119
  8. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 55
  9. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  10. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 10; 2026: 19.

29 papers.

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  2. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learningcited by 52
  3. 2025Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Basescited by 11
  4. 2025BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answeringcited by 10
  5. 2025Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?cited by 8
  6. 2025GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databasescited by 7
  7. 2026GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Basescited by 7
  8. 2025RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learningcited by 6
  9. 2026Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansioncited by 5
  10. 2025Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledgecited by 3
  11. 2025GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generationcited by 3
  12. 2026Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrievalcited by 3
17 more
  1. 2025See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Modelscited by 2
  2. 2026CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scalecited by 2
  3. 2026Disco-RAG: Discourse-Aware Retrieval-Augmented Generationcited by 1
  4. 2026Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Datacited by 0
  5. 2026Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Researchcited by 0
  6. 2026Training-Free Test-Time Contrastive Learning for Large Language Modelscited by 0
  7. 2026RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generationcited by 0
  8. 2026GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAGcited by 0
  9. 2026LivingRAG: Augmenting Graph RAG with Experiencecited by 0
  10. 2026R2Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAGcited by 0
  11. 2026Building Trustworthy Graph-Agentic RAG for Social Good: Architectures, Failure Propagation, and Assurance by Constructioncited by 0
  12. 2026Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergycited by 0
  13. 2026FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Explorationcited by 0
  14. 2026Beyond Similarity through Zero-Token Geometric Graphs for Multi-Hop RAGcited by 0
  15. 2026Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoningcited by 0
  16. 2026Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrievalcited by 0
  17. 2026Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolvecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Augmenting Question Answering with A Hybrid RAG Approach
  2. 2026HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
  3. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  4. 2025PASemiQA: Plan-Assisted Agent for Question Answering on Semi-Structured Data with Text and Relational Information
  5. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
  6. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  7. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  8. 2025Question Decomposition for Retrieval-Augmented Generation