mlsanityData to 7 Oct 2026

Survey of Hallucination in Natural Language Generation

arXiv 2202.03629 ↗First posted 8 Feb 2022cs.CLcited by 2,056

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Cited by 213 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation; and (3) hallucinations in large language models (LLMs). This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

Key facts. Cited by 2,056 papers in the map (data through 7 Oct 2026). Builds on 189 of its 253 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

189 of 253 references.

References · 189

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 48; 2023: 425; 2024: 457; 2025: 530; 2026: 596.

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  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  3. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  4. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  6. 2023PaLM 2 Technical Reportcited by 1,154
  7. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  8. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  9. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  10. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
  11. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  12. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
38 more
  1. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 671
  2. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  3. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  4. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  5. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  6. 2024Large Language Models: A Surveycited by 506
  7. 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
  8. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
  9. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
  10. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  11. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 367
  12. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  13. 2024A Survey on Hallucination in Large Vision-Language Modelscited by 352
  14. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  15. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  16. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  17. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  18. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  19. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  20. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 317
  21. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  22. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  23. 2023Effective Long-Context Scaling of Foundation Modelscited by 301
  24. 2022Teaching Small Language Models to Reasoncited by 279
  25. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  26. 2022Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptorscited by 267
  27. 2023Detecting and Preventing Hallucinations in Large Vision Language Modelscited by 254
  28. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  29. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  30. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
  31. 2024VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videoscited by 242
  32. 2023Large Language Model Alignment: A Surveycited by 241
  33. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddingscited by 238
  34. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 238
  35. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 237
  36. 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 230
  37. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  38. 2024Tool Learning with Large Language Models: A Surveycited by 221

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