相关实验视频

Updated: Jun 23, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

543

医疗教育中安全有效的大型语言模型的成本,可用性,可信度,公平性,问责制,透明度和可解释性框架:叙述审查和定性研究

Majdi Quttainah1, Vinaytosh Mishra2, Somayya Madakam3

  • 1College of Business Administration, Kuwait University, Kuwait, Kuwait.

JMIR AI
|June 14, 2024
PubMed
概括

信誉,问责和公平是医学教育中开发大型语言模型 (LLM) 的关键推动因素. 一个新的框架,CUC-FATE,指导对有效实施LLM的这些关键因素的评估.

关键词:
在 AHP AHP 中.在CUC-FATE框架下.聊天GPT 聊天GPT 的意思法学士 (LLM) 是一个专业.这就是TISM.通过通过通过通过.分析层次的过程分析层次的过程.聊天生成预训练变压器成本,可用性,可信性,公平性,问责性,透明度和可解释性.数据生成数据的数据生成.发展发展发展发展发展.生成性语言模型工具.准则 准则 准则医疗保健 医疗保健 医疗保健医疗保健专业人员的医疗保健专业人员创新 创新 创新 创新 创新 创新大型语言模型医学教育 医学教育叙事审查 叙事审查总的解释性结构建模.用户用户用户用户用户.

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