mlsanityData to 7 Oct 2026

Med-HALT: Medical Domain Hallucination Test for Large Language Models

arXiv 2307.15343 ↗First posted 28 Jul 2023cs.CL cs.AI cs.LGcited by 156

Not on the map. Why?

Cited by 20 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. Hallucination, wherein these models generate plausible yet unverified or incorrect information, can have serious consequences in healthcare applications. We propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate and reduce hallucinations. Med-HALT provides a diverse multinational dataset derived from medical examinations across various countries and includes multiple innovative testing modalities. Med-HALT includes two categories of tests reasoning and memory-based hallucination tests, designed to assess LLMs's problem-solving and information retrieval abilities. Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa-2, MPT, and Falcon, revealing significant differences in their performance. The paper provides detailed insights into the dataset, promoting transparency and reproducibility. Through this work, we aim to contribute to the development of safer and more reliable language models in healthcare. Our benchmark can be found at medhalt.github.io

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 28 Jul 2023.

Builds on

20 of 35 references.

References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  5. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  7. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  8. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  9. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  10. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  11. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
  12. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
8 more
  1. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  2. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 525
  3. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  4. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  5. 2023MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Datacited by 282
  6. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 206
  7. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  8. 2019HEAD-QA: A Healthcare Dataset for Complex Reasoningcited by 100

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 57; 2025: 46; 2026: 41.

156 papers.

Sort:

By area:

  1. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  2. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 311
  3. 2024Capabilities of Gemini Models in Medicinecited by 231
  4. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoningcited by 145
  5. 2023Large language models in healthcare and medical domain: A reviewcited by 126
  6. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  7. 2023Towards Accurate Differential Diagnosis with Large Language Modelscited by 115
  8. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  9. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 109
  10. 2024Unfamiliar Finetuning Examples Control How Language Models Hallucinatecited by 85
  11. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 85
  12. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
38 more
  1. 2024Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancementcited by 66
  2. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  3. 2025Reasoning Models Better Express Their Confidencecited by 63
  4. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  5. 2025Steer LLM Latents for Hallucination Detectioncited by 49
  6. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  7. 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
  8. 2023Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucinationcited by 41
  9. 2024A Survey on Large Language Model Hallucination via a Creativity Perspectivecited by 38
  10. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  11. 2024Knowledge Boundary of Large Language Models: A Surveycited by 36
  12. 2024Zero-Shot Clinical Trial Patient Matching with LLMscited by 35
  13. 2024LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learningcited by 34
  14. 2024History, Development, and Principles of Large Language Models-An Introductory Surveycited by 30
  15. 2024HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoningcited by 30
  16. 2024Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspectivecited by 29
  17. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  18. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27
  19. 2024Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinationscited by 26
  20. 2023UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generationcited by 22
  21. 2024A Survey of Conversational Searchcited by 22
  22. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Datacited by 21
  23. 2024How well do LLMs cite relevant medical references? An evaluation framework and analysescited by 20
  24. 2024Aloe: A Family of Fine-tuned Open Healthcare LLMscited by 20
  25. 2024OLAPH: Improving Factuality in Biomedical Long-form Question Answeringcited by 20
  26. 2024BeHonest: Benchmarking Honesty in Large Language Modelscited by 20
  27. 2024MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Modelscited by 20
  28. 2024Hallucination Benchmark in Medical Visual Question Answeringcited by 18
  29. 2025Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Modelscited by 18
  30. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  31. 2024From Single to Multi: How LLMs Hallucinate in Multi-Document Summarizationcited by 17
  32. 2024Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contextscited by 15
  33. 2024Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Modelscited by 15
  34. 2024CigaR: Cost-efficient Program Repair with LLMscited by 14
  35. 2025SINdex: Semantic INconsistency Index for Hallucination Detection in LLMscited by 14
  36. 2025Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoningcited by 14
  37. 2024ArgMed-Agents: Explainable Clinical Decision Reasoning with LLM Disscusion via Argumentation Schemescited by 13
  38. 2024Look Within, Why LLMs Hallucinate: A Causal Perspectivecited by 11

50 most cited of 156 All 156 (JSON)