mlsanityData to 7 Oct 2026

Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models

arXiv 2408.13808 ↗First posted 25 Aug 2024cs.CLcited by 15

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Abstract (arXiv)

The rapid advancement of large language models (LLMs) has significantly impacted various domains, including healthcare and biomedicine. However, the phenomenon of hallucination, where LLMs generate outputs that deviate from factual accuracy or context, poses a critical challenge, especially in high-stakes domains. This paper conducts a scoping study of existing techniques for mitigating hallucinations in knowledge-based task in general and especially for medical domains. Key methods covered in the paper include Retrieval-Augmented Generation (RAG)-based techniques, iterative feedback loops, supervised fine-tuning, and prompt engineering. These techniques, while promising in general contexts, require further adaptation and optimization for the medical domain due to its unique demands for up-to-date, specialized knowledge and strict adherence to medical guidelines. Addressing these challenges is crucial for developing trustworthy AI systems that enhance clinical decision-making and patient safety as well as accuracy of biomedical scientific research.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 25 Aug 2024.

Builds on

34 of 47 references.

References · 34

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  4. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  5. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  6. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  7. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
  8. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 349
  9. 2023How Language Model Hallucinations Can Snowballcited by 310
  10. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  11. 2023Large Language Model Unlearningcited by 288
  12. 2024TrustLLM: Trustworthiness in Large Language Modelscited by 280
22 more
  1. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 267
  2. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  3. 2022Prompting GPT-3 To Be Reliablecited by 258
  4. 2023Fine-tuning Language Models for Factualitycited by 219
  5. 2024Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judgecited by 202
  6. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  7. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  8. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
  9. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  10. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  11. 2024BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Textcited by 85
  12. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 74
  13. 2019Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generationcited by 62
  14. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  15. 2023Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinationscited by 34
  16. 2023Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AIcited by 33
  17. 2021Focus Attention: Promoting Faithfulness and Diversity in Summarizationcited by 30
  18. 2024RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processingcited by 25
  19. 2024When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectivelycited by 20
  20. 2024Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrievalcited by 16
  21. 2023LLMMaps -- A Visual Metaphor for Stratified Evaluation of Large Language Modelscited by 6
  22. 2023A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Modelscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 10; 2026: 3.

15 papers.

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  1. 2024Disentangling Memory and Reasoning Ability in Large Language Modelscited by 43
  2. 2025RARE: Retrieval-Augmented Reasoning Modelingcited by 11
  3. 2025Efficient Beam Search for Large Language Models Using Trie-Based Decodingcited by 10
  4. 2025End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoningcited by 10
  5. 2025LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Modelscited by 9
  6. 2025Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decodingcited by 6
  7. 2025BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMscited by 4
  8. 2024LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languagescited by 3
  9. 2025Hallucination, Monofacts, and Miscalibration: An Empirical Investigationcited by 1
  10. 2025MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answeringcited by 1
  11. 2026Planner-Auditor Twin: Agentic Discharge Planning with FHIR-Based LLM Planning, Guideline Recall, Optional Caching and Self-Improvementcited by 1
  12. 2026Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steeringcited by 1
3 more
  1. 2026VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answeringcited by 1
  2. 2025FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identificationcited by 0
  3. 2025Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRexcited by 0

Similar, not cited

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  1. 2024HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making
  2. 2026Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
  3. 2024Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots
  4. 2024MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
  5. 2025Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation
  6. 2024Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
  7. 2025Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
  8. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answering