mlsanityData to 14 Oct 2026

The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

arXiv 2310.04988 ↗First posted 8 Oct 2023cs.AIcited by 131

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Abstract (arXiv)

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (HILT), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations.

Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

30 of 61 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  8. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  9. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  10. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  11. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  12. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
18 more
  1. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  2. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  3. 2020GLU Variants Improve Transformercited by 1,804
  4. 2019Root Mean Square Layer Normalizationcited by 1,480
  5. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  6. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  7. 2019Fast Transformer Decoding: One Write-Head is All You Needcited by 717
  8. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 206
  9. 2022Factuality Enhanced Language Models for Open-Ended Text Generationcited by 202
  10. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  11. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 129
  12. 2023Do Language Models Know When They're Hallucinating References?cited by 116
  13. 2023Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learningcited by 115
  14. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  15. 2023PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptionscited by 53
  16. 2023mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinationscited by 24
  17. 2022Continuous-Time Meta-Learning with Forward Mode Differentiationcited by 12
  18. 2022Improved Beam Search for Hallucination Mitigation in Abstractive Summarizationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 8; 2024: 51; 2025: 50; 2026: 22.

131 papers.

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  1. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  2. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  3. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  4. 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 244
  5. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 149
  6. 2024CRAG -- Comprehensive RAG Benchmarkcited by 97
  7. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  8. 2024Factuality of Large Language Models: A Surveycited by 66
  9. 2024MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learningcited by 63
  10. 2024Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden Statescited by 52
  11. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  12. 2024Memory3: Language Modeling with Explicit Memorycited by 40
38 more
  1. 2024Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applicationscited by 31
  2. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  3. 2023FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicitycited by 25
  4. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Argumentscited by 25
  5. 2024Prompting open-source and commercial language models for grammatical error correction of English learner textcited by 24
  6. 2023UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generationcited by 22
  7. 2024Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)cited by 20
  8. 2025RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discoverycited by 19
  9. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challengescited by 19
  10. 2025MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Frameworkcited by 18
  11. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  12. 2024From Single to Multi: How LLMs Hallucinate in Multi-Document Summarizationcited by 17
  13. 2025Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViTcited by 17
  14. 2025MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Themcited by 16
  15. 2024Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Modelscited by 15
  16. 2024Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citationscited by 15
  17. 2025Differentially Private Steering for Large Language Model Alignmentcited by 15
  18. 2023Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safetycited by 14
  19. 2024MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languagescited by 14
  20. 2024LLM-based agents for automating the enhancement of user story quality: An early reportcited by 13
  21. 2024LLMScan: Causal Scan for LLM Misbehavior Detectioncited by 13
  22. 2025ProAPO: Progressively Automatic Prompt Optimization for Visual Classificationcited by 13
  23. 2025Uncertainty Distillation: Teaching Language Models to Express Semantic Confidencecited by 13
  24. 2024Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinationscited by 12
  25. 2024FactAlign: Long-form Factuality Alignment of Large Language Modelscited by 12
  26. 2025Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributionscited by 12
  27. 2024Visual Hallucination: Definition, Quantification, and Prescriptive Remediationscited by 11
  28. 2024Building a Large Japanese Web Corpus for Large Language Modelscited by 11
  29. 2024REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropycited by 11
  30. 2024Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropycited by 11
  31. 2024FACTOID: FACtual enTailment fOr hallucInation Detectioncited by 10
  32. 2025Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systemscited by 10
  33. 2024Collaborative decoding of critical tokens for boosting factuality of large language modelscited by 9
  34. 2025MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaborationcited by 9
  35. 2023Advancing TTP Analysis: Harnessing the Power of Large Language Models with Retrieval Augmented Generationcited by 8
  36. 2024'Quis custodiet ipsos custodes?' Who will watch the watchmen? On Detecting AI-generated peer-reviewscited by 8
  37. 2025Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detectioncited by 8
  38. 2024Evaluating Consistency and Reasoning Capabilities of Large Language Modelscited by 7

50 most cited of 131 All 131 (JSON)