mlsanityData to 14 Oct 2026

Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

arXiv 2310.07521 ↗First posted 11 Oct 2023cs.CLcited by 206

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Cited by 38 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies, highlighting the potential consequences and challenges posed by factual errors in LLM outputs. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality, including approaches tailored for specific domains. We focus two primary LLM configurations standalone LLMs and Retrieval-Augmented LLMs that utilizes external data, we detail their unique challenges and potential enhancements. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs.

Key facts. Cited by 206 papers in the map (data through 14 Oct 2026). Builds on 211 of its 283 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

211 of 283 references.

References · 211

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50 most cited of 211 All 211 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 13; 2024: 98; 2025: 67; 2026: 28.

206 papers.

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  1. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  2. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  3. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  4. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 178
  5. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 150
  6. 2024Language Models with Conformal Factuality Guaranteescited by 131
  7. 2024AutoSurvey: Large Language Models Can Automatically Write Surveyscited by 119
  8. 2025HalluLens: LLM Hallucination Benchmarkcited by 92
  9. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  10. 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communitiescited by 86
  11. 2023Alleviating Hallucinations of Large Language Models through Induced Hallucinationscited by 79
  12. 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 77
38 more
  1. 2024Unified Hallucination Detection for Multimodal Large Language Modelscited by 75
  2. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agentscited by 72
  3. 2024Factuality of Large Language Models: A Surveycited by 66
  4. 2025Evaluating Step-by-step Reasoning Traces: A Surveycited by 63
  5. 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 61
  6. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generationcited by 61
  7. 2024Can Language Models Learn to Skip Steps?cited by 59
  8. 2023LLatrieval: LLM-Verified Retrieval for Verifiable Generationcited by 52
  9. 2024NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surroundingcited by 51
  10. 2024Knowledge Mechanisms in Large Language Models: A Survey and Perspectivecited by 51
  11. 2024Can AI Assistants Know What They Don't Know?cited by 50
  12. 2024Context-DPO: Aligning Language Models for Context-Faithfulnesscited by 50
  13. 2024In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigationcited by 45
  14. 2024Disentangling Memory and Reasoning Ability in Large Language Modelscited by 44
  15. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  16. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 39
  17. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalitiescited by 38
  18. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systemscited by 38
  19. 2024IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neuronscited by 38
  20. 2025DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrievalcited by 37
  21. 2024KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Modelscited by 36
  22. 2024SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generationcited by 34
  23. 2025Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoningcited by 33
  24. 2024JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capabilitycited by 32
  25. 2024SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphscited by 31
  26. 2024Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Modelscited by 30
  27. 2024Logically Consistent Language Models via Neuro-Symbolic Integrationcited by 30
  28. 2025AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decodingcited by 30
  29. 2023FactCHD: Benchmarking Fact-Conflicting Hallucination Detectioncited by 29
  30. 2024Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrievalcited by 29
  31. 2025RewardAnything: Generalizable Principle-Following Reward Modelscited by 28
  32. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wildcited by 27
  33. 2023StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understandingcited by 26
  34. 2024Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Modelscited by 26
  35. 2023Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMscited by 24
  36. 2024Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Datacited by 24
  37. 2023RELIC: Investigating Large Language Model Responses using Self-Consistencycited by 23
  38. 2023UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generationcited by 22

50 most cited of 206 All 206 (JSON)

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  5. 2025Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
  6. 2024How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency
  7. 2026Do LLMs Make More Mistakes If They Do Not Believe the Input Data?
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