mlsanityData to 7 Oct 2026

Self-Knowledge Guided Retrieval Augmentation for Large Language Models

arXiv 2310.05002 ↗First posted 8 Oct 2023cs.CLcited by 69

Not on the map. Why?

Cited by 35 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be incomplete and difficult to update due to the computational costs. As complementary, retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. However, we find that the retrieved knowledge does not always help and even has a negative impact on original responses occasionally. To better make use of both internal knowledge and external world knowledge, we investigate eliciting the model's ability to recognize what they know and do not know (which is also called self-knowledge) and propose Self-Knowledge guided Retrieval augmentation (SKR), a simple yet effective method which can let LLMs refer to the questions they have previously encountered and adaptively call for external resources when dealing with new questions. We evaluate SKR on multiple datasets and demonstrate that it outperforms chain-of-thought based and fully retrieval-based methods by using either InstructGPT or ChatGPT.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Builds on 53 of its 62 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

53 of 62 references.

References · 53

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  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. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  10. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  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
38 more
  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  2. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  4. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  5. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  7. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  8. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  9. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  10. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  11. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  12. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  13. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  14. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  15. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  16. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  17. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  18. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  19. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
  20. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  21. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  22. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  23. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
  24. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  25. 2020Recipes for building an open-domain chatbotcited by 593
  26. 2020Fact or Fiction: Verifying Scientific Claimscited by 546
  27. 2023Active Retrieval Augmented Generationcited by 506
  28. 2023Augmented Language Models: a Surveycited by 398
  29. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  30. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  31. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  32. 2023Do Large Language Models Know What They Don't Know?cited by 238
  33. 2020Nearest Neighbor Machine Translationcited by 209
  34. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  35. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  36. 2020INFOTABS: Inference on Tables as Semi-structured Datacited by 84
  37. 2023kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inferencecited by 47
  38. 2023WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpuscited by 29

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 28; 2025: 29; 2026: 11.

69 papers.

Sort:

By area:

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  4. 2025R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learningcited by 322
  5. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  6. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  7. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 157
  8. 2024A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthinesscited by 125
  9. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  10. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  11. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  12. 2024Repoformer: Selective Retrieval for Repository-Level Code Completioncited by 55
38 more
  1. 2025Test-Time Learning for Large Language Modelscited by 46
  2. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generationcited by 43
  3. 2024Goldfish: Vision-Language Understanding of Arbitrarily Long Videoscited by 38
  4. 2024Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questionscited by 30
  5. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  6. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  7. 2025Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicingcited by 24
  8. 2024On Mitigating Code LLM Hallucinations with API Documentationcited by 21
  9. 2024A Survey on the Honesty of Large Language Modelscited by 21
  10. 2024Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answeringcited by 20
  11. 2024SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedbackcited by 19
  12. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  13. 2025FinSage: A Multi-aspect RAG System for Financial Filings Question Answeringcited by 18
  14. 2025Empowering GraphRAG with Knowledge Filtering and Integrationcited by 15
  15. 2024FIT-RAG: Black-Box RAG with Factual Information and Token Reductioncited by 13
  16. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  17. 2024Human-inspired Perspectives: A Survey on AI Long-term Memorycited by 12
  18. 2024KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Modelscited by 12
  19. 2024CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checkingcited by 11
  20. 2024Right this way: Can VLMs Guide Us to See More to Answer Questions?cited by 10
  21. 2024Evaluating the External and Parametric Knowledge Fusion of Large Language Modelscited by 9
  22. 2025C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generationcited by 9
  23. 2025Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalizationcited by 9
  24. 2024Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Informationcited by 8
  25. 2026DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agentscited by 7
  26. 2026Position: Modular Memory is the Key to Continual Learning Agentscited by 7
  27. 2025SUGAR: Leveraging Contextual Confidence for Smarter Retrievalcited by 6
  28. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Controlcited by 6
  29. 2025Enterprise Large Language Model Evaluation Benchmarkcited by 5
  30. 2025Impact-driven Context Filtering For Cross-file Code Completioncited by 5
  31. 2025KnowRL: Teaching Language Models to Know What They Knowcited by 5
  32. 2026Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryptioncited by 4
  33. 2024LLM Augmentations to support Analytical Reasoning over Multiple Documentscited by 3
  34. 2025To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generationcited by 3
  35. 2025Can an LLM Induce a Graph? Investigating Memory Drift and Context Lengthcited by 3
  36. 2025A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systemscited by 3
  37. 2026Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)cited by 3
  38. 2026MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answeringcited by 3

50 most cited of 69 All 69 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
  2. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
  3. 2025SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation
  4. 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
  5. 2026Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
  6. 2025Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection
  7. 2024KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models
  8. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories