mlsanityData to 7 Oct 2026

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

arXiv 2305.15294 ↗First posted 24 May 2023cs.CLcited by 343

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connecting them to the world. Retrieval-augmented large language models have raised extensive attention for grounding model generation on external knowledge. However, retrievers struggle to capture relevance, especially for queries with complex information needs. Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to improve retrieval with generation. In this paper, we show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge which in turn helps generate a better output in the next iteration. Compared with recent work which interleaves retrieval with generation when producing an output, Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints. We evaluate Iter-RetGen on multi-hop question answering, fact verification, and commonsense reasoning, and show that it can flexibly leverage parametric knowledge and non-parametric knowledge, and is superior to or competitive with state-of-the-art retrieval-augmented baselines while causing fewer overheads of retrieval and generation. We can further improve performance via generation-augmented retrieval adaptation.

Key facts. Cited by 343 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 33 of its 40 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

33 of 40 references.

References · 33

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21 more
  1. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  2. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  3. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  4. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  5. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  6. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  7. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  8. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
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  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  12. 2023Active Retrieval Augmented Generationcited by 506
  13. 2023Augmented Language Models: a Surveycited by 398
  14. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
  15. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  16. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  17. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  18. 2022Task-aware Retrieval with Instructionscited by 107
  19. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87
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  21. 2021Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Frameworkcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 15; 2024: 104; 2025: 131; 2026: 93.

343 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  4. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
  5. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  6. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  7. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  8. 2025R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learningcited by 322
  9. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
  10. 2023ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solvingcited by 276
  11. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 273
  12. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
38 more
  1. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  2. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  3. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  4. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  5. 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learningcited by 152
  6. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  7. 2025MMSearch-R1: Incentivizing LMMs to Searchcited by 120
  8. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  9. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  10. 2023Self-Knowledge Guided Retrieval Augmentation for Large Language Modelscited by 69
  11. 2025Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learningcited by 68
  12. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  13. 2025In-the-Flow Agentic System Optimization for Effective Planning and Tool Usecited by 67
  14. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  15. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generationcited by 63
  16. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
  17. 2025SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesiscited by 55
  18. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questionscited by 54
  19. 2023LLatrieval: LLM-Verified Retrieval for Verifiable Generationcited by 52
  20. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Datacited by 52
  21. 2025OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignmentcited by 51
  22. 2025From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agentscited by 49
  23. 2025Chain-of-Retrieval Augmented Generationcited by 48
  24. 2024RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedbackcited by 46
  25. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  26. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 41
  27. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Modelscited by 41
  28. 2024Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approachcited by 40
  29. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learningcited by 39
  30. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systemscited by 38
  31. 2024HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systemscited by 37
  32. 2024Knowledge Boundary of Large Language Models: A Surveycited by 36
  33. 2024Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Modelscited by 34
  34. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMscited by 34
  35. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  36. 2023How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advancescited by 33
  37. 2025R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learningcited by 32
  38. 2024Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questionscited by 30

50 most cited of 343 All 343 (JSON)

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  3. 2026Latent Abstraction for Retrieval-Augmented Generation
  4. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  5. 2026OThink-SRR1: Search, Refine and Reasoning with Reinforced Learning for Large Language Models
  6. 2025DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
  7. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
  8. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation