mlsanityData to 14 Oct 2026

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models

arXiv 2311.00287 ↗First posted 1 Nov 2023cs.CL cs.AI cs.LGcited by 20

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Abstract (arXiv)

Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical NLP tasks. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our model involves clinical knowledge extraction and context-informed LLM prompting. Both clinical topics and writing styles are drawn from external domain-specific knowledge graphs and LLMs to guide data generation. Our extensive empirical study across 7 clinical NLP tasks and 16 datasets reveals that ClinGen consistently enhances performance across various tasks, effectively aligning the distribution of real datasets and significantly enriching the diversity of generated training instances. Our code is available at urlhttps://github.com/ritaranx/ClinGen.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 68 of its 107 references that are in the map. First posted on arXiv 1 Nov 2023.

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68 of 107 references.

References · 68

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Citing papers per year: 2023: 2; 2024: 2; 2025: 14; 2026: 2.

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  10. 2025A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectivescited by 3
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  3. 2025Cross-modal Causal Intervention for Alzheimer's Disease Predictioncited by 1
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  6. 2025AutoGeTS: Knowledge-based Automated Generation of Text Synthetics for Improving Text Classificationcited by 0
  7. 2025Expert-guided Clinical Text Augmentation via Query-Based Model Collaborationcited by 0
  8. 2026Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languagescited by 0