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Updated: May 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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使用合成医疗保健数据来利用大型语言模型进行命名实体识别:开发和验证研究

Hendrik Šuvalov1, Mihkel Lepson1, Veronika Kukk1

  • 1Institute of Computer Science, University of Tartu, Tartu, Estonia.

Journal of medical Internet research
|March 18, 2025
PubMed
概括

本研究引入了一种创新的方法,用于创建爱沙尼亚医疗命名实体识别 (NER) 模型,使用由大语言模型 (LLM) 生成的合成数据. 这种方法克服了低资源语言的数据稀缺性,同时保持了患者的隐私.

关键词:
爱沙尼亚语 爱沙尼亚语在法学士 (LLM) 课程中.尼尔·内尔 (NER NER NER) 是一个国家.在NLP中,我们使用了NLP.有注释的数据.人工智能的人工智能是人工智能.临床决策支持 临床决策支持数据注释数据注释数据挖掘是数据挖掘的一个方法.医疗保健数据 医疗保健数据语言模型语言模型大型语言模型机器学习是机器学习.医疗实体是一个医疗实体.命名实体的认可 命名实体的认可自然语言处理自然语言处理.综合数据 综合数据

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