mlsanityData to 7 Oct 2026

MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

arXiv 2012.13978 ↗First posted 27 Dec 2020cs.CL cs.AI cs.LGcited by 18

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Abstract (arXiv)

One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 8; 2025: 2; 2026: 1.

18 papers.

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  1. 2024Lossless data compression by large modelscited by 20
  2. 2024Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarkscited by 17
  3. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  4. 2021Fine-Tuning Transformers: Vocabulary Transfercited by 14
  5. 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
  6. 2021MadDog: A Web-based System for Acronym Identification and Disambiguationcited by 8
  7. 2024RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Educationcited by 7
  8. 2024MedINST: Meta Dataset of Biomedical Instructionscited by 7
  9. 2025Uncertainty-aware abstention in medical diagnosis based on medical textscited by 6
  10. 2023GLADIS: A General and Large Acronym Disambiguation Benchmarkcited by 5
  11. 2024Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Fieldcited by 5
  12. 2022Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemiacited by 3
6 more
  1. 2025Improving Privacy Benefits of Redactioncited by 2
  2. 2022Token Classification for Disambiguating Medical Abbreviationscited by 0
  3. 2023Echoes of Biases: How Stigmatizing Language Affects AI Performancecited by 0
  4. 2024SPOT: Text Source Prediction from Originality Score Thresholdingcited by 0
  5. 2024Towards Quantifying The Privacy Of Redacted Textcited by 0
  6. 2026PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguationcited by 0