mlsanityData to 7 Oct 2026

Hallucination Detection and Hallucination Mitigation: An Investigation

arXiv 2401.08358 ↗First posted 16 Jan 2024cs.CL cs.AIcited by 20

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Abstract (arXiv)

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 46 of its 59 references that are in the map. First posted on arXiv 16 Jan 2024.

Builds on

46 of 59 references.

References · 46

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
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  11. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  12. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
34 more
  1. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  2. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  3. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  4. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  5. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  7. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  8. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
  9. 2015Generating Sentences from a Continuous Spacecited by 607
  10. 2019Text Summarization with Pretrained Encoderscited by 553
  11. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  12. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  13. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
  14. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  15. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
  16. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  17. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  18. 2021QuestEval: Summarization Asks for Fact-based Evaluationcited by 241
  19. 2019Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoringcited by 164
  20. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  21. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  22. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  23. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  24. 2020Evaluating Factuality in Generation with Dependency-level Entailmentcited by 122
  25. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  26. 2021Learning Compact Metrics for MTcited by 93
  27. 2021Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Featurescited by 74
  28. 2022RHO (ρ): Reducing Hallucination in Open-domain Dialogues with Knowledge Groundingcited by 47
  29. 2020Generating Representative Headlines for News Storiescited by 39
  30. 2022Contrastive Learning Reduces Hallucination in Conversationscited by 39
  31. 2021Prevent the Language Model from being Overconfident in Neural Machine Translationcited by 34
  32. 2023MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarizationcited by 30
  33. 2019Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networkscited by 12
  34. 2022HaRiM^+: Evaluating Summary Quality with Hallucination Riskcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 10; 2026: 3.

20 papers.

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  1. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  2. 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
  3. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 38
  4. 2024Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detectorcited by 20
  5. 2024DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Modelscited by 15
  6. 2024Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awarenesscited by 13
  7. 2025HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detectioncited by 13
  8. 2025Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Modelscited by 9
  9. 2025LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signalscited by 8
  10. 2025Evaluating Evaluation Metrics -- The Mirage of Hallucination Detectioncited by 6
  11. 2025Span-Level Hallucination Detection for LLM-Generated Answerscited by 5
  12. 2024A Unified Hallucination Mitigation Framework for Large Vision-Language Modelscited by 3
8 more
  1. 2025Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigationcited by 3
  2. 2025Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioningcited by 2
  3. 2026Structure Guided Retrieval-Augmented Generation for Factual Queriescited by 2
  4. 2024An Evolutionary Large Language Model for Hallucination Mitigationcited by 0
  5. 2025OnionEval: An Unified Evaluation of Fact-conflicting Hallucination for Small-Large Language Modelscited by 0
  6. 2025Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detectioncited by 0
  7. 2026Rethinking Evaluation for LLM Hallucination Detection: A Desiderata, A New RAG-based Benchmark, New Insightscited by 0
  8. 2026HIVE: Understanding Post-Hallucination Reasoning in Vision Language Modelscited by 0