Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909465539772416 |
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| author | Xu, Ran Cui, Hejie Yu, Yue Kan, Xuan Shi, Wenqi Zhuang, Yuchen Jin, Wei Ho, Joyce Yang, Carl |
| author_facet | Xu, Ran Cui, Hejie Yu, Yue Kan, Xuan Shi, Wenqi Zhuang, Yuchen Jin, Wei Ho, Joyce Yang, Carl |
| contents | 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 \url{https://github.com/ritaranx/ClinGen}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00287 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models Xu, Ran Cui, Hejie Yu, Yue Kan, Xuan Shi, Wenqi Zhuang, Yuchen Jin, Wei Ho, Joyce Yang, Carl Computation and Language Artificial Intelligence Machine Learning Quantitative Methods 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 \url{https://github.com/ritaranx/ClinGen}. |
| title | Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2311.00287 |