mlsanityData to 7 Oct 2026

Active Retrieval Augmented Generation

arXiv 2305.06983 ↗First posted 11 May 2023cs.CL cs.LGcited by 506

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. This is limiting, however, in more general scenarios involving generation of long texts, where continually gathering information throughout generation is essential. In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content, which is then utilized as a query to retrieve relevant documents to regenerate the sentence if it contains low-confidence tokens. We test FLARE along with baselines comprehensively over 4 long-form knowledge-intensive generation tasks/datasets. FLARE achieves superior or competitive performance on all tasks, demonstrating the effectiveness of our method. Code and datasets are available at https://github.com/jzbjyb/FLARE.

Key facts. Cited by 506 papers in the map (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 56 of its 79 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

56 of 79 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023In-Context Retrieval-Augmented Language Models31 Jan
  2. 2023REPLUG: Retrieval-Augmented Black-Box Language Models30 Jan
  3. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP28 Dec
  4. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Models5 Aug
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Training10 Feb

Other references · 50

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  8. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  9. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  10. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  11. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  12. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
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  1. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  2. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  3. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  4. 2019Language Models as Knowledge Bases?cited by 1,922
  5. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  7. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  8. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  9. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  10. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  11. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  12. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  13. 2019How Can We Know What Language Models Know?cited by 953
  14. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  15. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  16. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
  17. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  18. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  19. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  20. 2019ELI5: Long Form Question Answeringcited by 567
  21. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  22. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  23. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  24. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
  25. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
  26. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  27. 2022Towards a Unified Multi-Dimensional Evaluator for Text Generationcited by 264
  28. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  29. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  30. 2021Hurdles to Progress in Long-form Question Answeringcited by 166
  31. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  32. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  33. 2022Recitation-Augmented Language Modelscited by 65
  34. 2020WikiAsp: A Dataset for Multi-domain Aspect-based Summarizationcited by 56
  35. 2022Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformercited by 45
  36. 2021You Only Need One Model for Open-domain Question Answeringcited by 17
  37. 2022Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsingcited by 15
  38. 2022Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?cited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 34; 2024: 129; 2025: 160; 2026: 183.

506 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  3. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  4. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  5. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  6. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
  7. 2023A Comprehensive Overview of Large Language Modelscited by 639
  8. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  9. 2024Large Language Models: A Surveycited by 506
  10. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  11. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  12. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
38 more
  1. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  4. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 273
  5. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 237
  6. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  7. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environmentscited by 215
  8. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  9. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  10. 2025ZeroSearch: Incentivize the Search Capability of LLMs without Searchingcited by 203
  11. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  12. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  13. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 143
  14. 2023Physics of Language Models: Part 3.2, Knowledge Manipulationcited by 142
  15. 2026SimpleMem: Efficient Lifelong Memory for LLM Agentscited by 140
  16. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 122
  17. 2024MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentationcited by 118
  18. 2024AutoSurvey: Large Language Models Can Automatically Write Surveyscited by 116
  19. 2023LawBench: Benchmarking Legal Knowledge of Large Language Modelscited by 111
  20. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 100
  21. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  22. 2025Maximizing Confidence Alone Improves Reasoningcited by 97
  23. 2025VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Usecited by 92
  24. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  25. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  26. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  27. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  28. 2024Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMscited by 79
  29. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Draftingcited by 76
  30. 2024Reliable, Adaptable, and Attributable Language Models with Retrievalcited by 73
  31. 2025Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMscited by 73
  32. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  33. 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 70
  34. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurizationcited by 70
  35. 2023Self-Knowledge Guided Retrieval Augmentation for Large Language Modelscited by 69
  36. 2025Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learningcited by 68
  37. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 65
  38. 2024Inference Scaling for Long-Context Retrieval Augmented Generationcited by 65

50 most cited of 506 All 506 (JSON)

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  1. 2024Retrieval is Accurate Generation
  2. 2024E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation
  3. 2024Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
  4. 2024Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
  5. 2026To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval
  6. 2024R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
  7. 2022A Survey on Retrieval-Augmented Text Generation
  8. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models