Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
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Abstract (arXiv)
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 28 Mar 2024.
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14 of 20 references.
References · 14
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
- 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
- 2020A Survey of Deep Active Learningcited by 554
- 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
- 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 207
- 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?cited by 154
- 2023Do We Still Need Clinical Language Models?cited by 98
- 2023Large Language Models to Identify Social Determinants of Health in Electronic Health Recordscited by 55
- 2023Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Sciencecited by 37
- 2023Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Datacited by 26
- 2023Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classificationcited by 17
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2 papers.