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Updated: Jun 20, 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

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灵丹:用大型语言模型增强对传统中医知识的编码,用于临床推理任务

Rui Hua1, Xin Dong1, Yu Wei2

  • 1Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.

Journal of the American Medical Informatics Association : JAMIA
|July 22, 2024
PubMed
概括

这项研究开发了传统中国医学 (TCM) 的灵丹大语言模型 (LLM),显著改进了人工智能驱动的诊断和处方建议. 灵丹模型在TCM临床推理任务中实现了最先进的性能.

关键词:
临床推理 临床推理大型语言模型预先培训的培训前培训处方建议建议的建议.传统的中国医药传统的中国医药.

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