mlsanityData to 14 Oct 2026

From Matching to Generation: A Survey on Generative Information Retrieval

arXiv 2404.14851 ↗First posted 23 Apr 2024cs.IR cs.AI cs.CLcited by 89

Not on the map. Why?

Cited by 26 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the advancement of pre-trained language models, generative information retrieval (GenIR) emerges as a novel paradigm, attracting increasing attention. Based on the form of information provided to users, current research in GenIR can be categorized into two aspects: (1) Generative Document Retrieval (GR) leverages the generative model's parameters for memorizing documents, enabling retrieval by directly generating relevant document identifiers without explicit indexing. (2) Reliable Response Generation employs language models to directly generate information users seek, breaking the limitations of traditional IR in terms of document granularity and relevance matching while offering flexibility, efficiency, and creativity to meet practical needs. This paper aims to systematically review the latest research progress in GenIR. We will summarize the advancements in GR regarding model training and structure, document identifier, incremental learning, etc., as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, etc. We also review the evaluation, challenges and future developments in GenIR systems. This review aims to offer a comprehensive reference for researchers, encouraging further development in the GenIR field. Github Repository: https://github.com/RUC-NLPIR/GenIR-Survey

Key facts. Cited by 89 papers in the map (data through 14 Oct 2026). Builds on 289 of its 376 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

289 of 376 references.

References · 289

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 12; 2025: 48; 2026: 29.

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  1. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 433
  2. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
  3. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 166
  4. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 83
  5. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  6. 2024"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Modelscited by 60
  7. 2025Chain-of-Retrieval Augmented Generationcited by 49
  8. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learningcited by 39
  9. 2024HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systemscited by 37
  10. 2024LLMs are Also Effective Embedding Models: An In-depth Overviewcited by 32
  11. 2025AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunitiescited by 28
  12. 2024A Survey of Conversational Searchcited by 22
38 more
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  2. 2024RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generationcited by 20
  3. 2025FinSage: A Multi-aspect RAG System for Financial Filings Question Answeringcited by 19
  4. 2025Hierarchical Document Refinement for Long-context Retrieval-augmented Generationcited by 16
  5. 2025It's High Time: A Survey of Temporal Question Answeringcited by 14
  6. 2025SSRL: Self-Search Reinforcement Learningcited by 14
  7. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
  8. 2025In-context Ranking Preference Optimizationcited by 9
  9. 2026Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimizationcited by 9
  10. 2025Personalization Toolkit: Training Free Personalization of Large Vision Language Modelscited by 8
  11. 2025IDMR: Towards Instance-Driven Precise Visual Correspondence in Multimodal Retrievalcited by 8
  12. 2025HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Searchcited by 8
  13. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  14. 2025QAgent: A modular Search Agent with Interactive Query Understandingcited by 8
  15. 2025LLM Alignment as Retriever Optimization: An Information Retrieval Perspectivecited by 7
  16. 2025Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Modelscited by 7
  17. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systemscited by 7
  18. 2025Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseasescited by 5
  19. 2025LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generationcited by 5
  20. 2026DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendationcited by 5
  21. 2026GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendationcited by 5
  22. 2025Alleviating LLM-based Generative Retrieval Hallucination in Alipay Searchcited by 4
  23. 2025CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collectionscited by 4
  24. 2025Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wildcited by 4
  25. 2025Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generationcited by 4
  26. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generationcited by 4
  27. 2025No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentationcited by 4
  28. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  29. 2024Generative Retrieval with Few-shot Indexingcited by 3
  30. 2024An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platformscited by 3
  31. 2024Invar-RAG: Invariant LLM-aligned Retrieval for Better Generationcited by 3
  32. 2025Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approachcited by 3
  33. 2025GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networkscited by 3
  34. 2025Purely Semantic Indexing for LLM-based Generative Recommendation and Retrievalcited by 3
  35. 2026OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generationcited by 3
  36. 2026IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimizationcited by 3
  37. 2026SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generationcited by 3
  38. 2024Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrievalcited by 2

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