mlsanityData to 7 Oct 2026

A Unified Definition of Hallucination: It's The World Model, Stupid!

arXiv 2512.21577 ↗First posted 25 Dec 2025cs.CL cs.AI cs.LGcited by 6

On the map: World models → World models for language agents

Abstract (arXiv)

Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. We argue that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we also connect our framework to HalluWorld, a complementary benchmark that instantiates fully specified reference world models for stress-testing model hallucinations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 69 of its 87 references that are in the map. First posted on arXiv 25 Dec 2025.

Builds on

69 of 87 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025CWM: An Open-Weights LLM for Research on Code Generation with World Models30 Sep
  2. 2024Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models21 Mar

Other references · 67

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  4. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  6. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  7. 2023HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Modelscited by 572
  8. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 551
  9. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 338
  10. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  11. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  12. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 267
38 more
  1. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  2. 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystackcited by 211
  3. 2020The NetHack Learning Environmentcited by 196
  4. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMscited by 174
  5. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  6. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 157
  7. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
  8. 2024RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learningcited by 146
  9. 2024BALROG: Benchmarking Agentic LLM and VLM Reasoning On Gamescited by 141
  10. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  11. 2023LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarizationcited by 96
  12. 2023Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedbackcited by 71
  13. 2021Chess as a Testbed for Language Model State Trackingcited by 69
  14. 2018Evaluating Theory of Mind in Question Answeringcited by 64
  15. 2024We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMscited by 61
  16. 2022Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translationcited by 60
  17. 2024Mitigating LLM Hallucinations via Conformal Abstentioncited by 56
  18. 2023Resolving Knowledge Conflicts in Large Language Modelscited by 49
  19. 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
  20. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 38
  21. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  22. 2023Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledgecited by 32
  23. 2025HALoGEN: Fantastic LLM Hallucinations and Where to Find Themcited by 32
  24. 2023FactCHD: Benchmarking Fact-Conflicting Hallucination Detectioncited by 29
  25. 2025FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generationcited by 27
  26. 2024To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contextscited by 25
  27. 2024Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretrainingcited by 24
  28. 2025DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMscited by 21
  29. 2024KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Modelscited by 20
  30. 2024Who's Who: Large Language Models Meet Knowledge Conflicts in Practicecited by 20
  31. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  32. 2024Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teachingcited by 17
  33. 2025Test-Time Adaptation for LLM Agents via Environment Interactioncited by 17
  34. 2025MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Themcited by 16
  35. 2023Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Modelscited by 15
  36. 2025Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoningcited by 15
  37. 2025Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planningcited by 15
  38. 2025SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearingcited by 14

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Citing papers per year: 2026: 6.

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  1. 2025Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
  2. 2025From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models
  3. 2026PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations
  4. 2025Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations
  5. 2026Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity
  6. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  7. 2026Hallucination in World Models is Predictable and Preventable
  8. 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models