mlsanityData to 7 Oct 2026

Lessons from Natural Language Inference in the Clinical Domain

arXiv 1808.06752 ↗First posted 21 Aug 2018cs.CLcited by 149

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Abstract (arXiv)

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.

Key facts. Cited by 149 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2018.

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Citing papers per year: 2021: 22; 2022: 19; 2023: 34; 2024: 30; 2025: 27; 2026: 17.

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  1. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  2. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  3. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  4. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  5. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 106
  6. 2021SciFive: a text-to-text transformer model for biomedical literaturecited by 99
  7. 2023Do We Still Need Clinical Language Models?cited by 97
  8. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  9. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  10. 2025A Survey of LLM-based Agents in Medicine: How far are we from Baymax?cited by 84
  11. 2021MS2: Multi-Document Summarization of Medical Studiescited by 80
  12. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 68
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  1. 2022Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencescited by 67
  2. 2023Metacognitive Prompting Improves Understanding in Large Language Modelscited by 58
  3. 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 53
  4. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  5. 2021Membership Inference Attack Susceptibility of Clinical Language Modelscited by 50
  6. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  7. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 46
  8. 2025Baichuan-M1: Pushing the Medical Capability of Large Language Modelscited by 41
  9. 2023ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Modelcited by 40
  10. 2023Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understandingcited by 40
  11. 2023Data Contamination Through the Lens of Timecited by 39
  12. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
  13. 2023Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generationcited by 37
  14. 2021GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domaincited by 36
  15. 2023A Comparative Study of Pretrained Language Models for Long Clinical Textcited by 35
  16. 2023Towards Unifying Medical Vision-and-Language Pre-training via Soft Promptscited by 35
  17. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  18. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  19. 2024A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?cited by 33
  20. 2021AMMU : A Survey of Transformer-based Biomedical Pretrained Language Modelscited by 32
  21. 2022Learning to Revise References for Faithful Summarizationcited by 28
  22. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 28
  23. 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?cited by 28
  24. 2025BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLPcited by 26
  25. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Datacited by 21
  26. 2022BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)cited by 20
  27. 2024SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trialscited by 20
  28. 2024OLAPH: Improving Factuality in Biomedical Long-form Question Answeringcited by 20
  29. 2025A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignmentcited by 20
  30. 2024MedSyn: LLM-based Synthetic Medical Text Generation Frameworkcited by 19
  31. 2021Rethinking Why Intermediate-Task Fine-Tuning Workscited by 18
  32. 2023NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reportscited by 18
  33. 2021MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domaincited by 17
  34. 2023Transformers in Healthcare: A Surveycited by 17
  35. 2024Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutionscited by 17
  36. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 17
  37. 2023DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generationcited by 16
  38. 2023Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processingcited by 16

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