mlsanityData to 7 Oct 2026

Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

arXiv 2302.12813 ↗First posted 24 Feb 2023cs.CL cs.AIcited by 351

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Cited by 59 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available.

Key facts. Cited by 351 papers in the map (data through 7 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

31 of 45 references.

References · 31

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  8. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  9. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  10. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  11. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  12. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
19 more
  1. 2021Ethical and social risks of harm from Language Modelscited by 957
  2. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  3. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  4. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  5. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  6. 2022Improving alignment of dialogue agents via targeted human judgementscited by 527
  7. 2022BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagecited by 197
  8. 2018Neural Approaches to Conversational AIcited by 164
  9. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  10. 2017A Knowledge-Grounded Neural Conversation Modelcited by 136
  11. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  12. 2022Training Language Models with Memory Augmentationcited by 112
  13. 2021A Comprehensive Assessment of Dialog Evaluation Metricscited by 90
  14. 2019Learning to Select Knowledge for Response Generation in Dialog Systemscited by 80
  15. 2020Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Accesscited by 58
  16. 2022GODEL: Large-Scale Pre-Training for Goal-Directed Dialogcited by 50
  17. 2022Neural Approaches to Conversational Information Retrievalcited by 41
  18. 2021RetGen: A Joint framework for Retrieval and Grounded Text Generation Modelingcited by 22
  19. 2022Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledgecited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 133; 2024: 127; 2025: 76; 2026: 15.

351 papers.

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  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  2. 2023A Survey of Large Language Modelscited by 2,537
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  4. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  5. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  6. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  7. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
  8. 2023Instruction Tuning with GPT-4cited by 676
  9. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  10. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  11. 2024Large Language Models: A Surveycited by 506
  12. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
38 more
  1. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  2. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  3. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 347
  4. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  5. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  6. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  7. 2023How Language Model Hallucinations Can Snowballcited by 310
  8. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  9. 2024CRUXEval: A Benchmark for Code Reasoning, Understanding and Executioncited by 294
  10. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
  11. 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 289
  12. 2023NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Modelscited by 274
  13. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  14. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  15. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  16. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
  17. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  18. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  19. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  20. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Modelscited by 223
  21. 2023Fine-tuning Language Models for Factualitycited by 219
  22. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?cited by 215
  23. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  24. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
  25. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  26. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 167
  27. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
  28. 2023SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoningcited by 157
  29. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 144
  30. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  31. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  32. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  33. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  34. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  35. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 122
  36. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
  37. 2023AutoMix: Automatically Mixing Language Modelscited by 116
  38. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 110

50 most cited of 351 All 351 (JSON)