通过令牌计数最小化,为医学教育提供LLMs的可持续推断
Hyunggu Jung1,2, Jiyoo Min3, Yunseo Moon3
1College of Nursing, Seoul National University, Seoul, Republic of Korea.
Studies in health technology and informatics
|August 8, 2025
概括
减少大型语言模型 (LLM) 的碳排放是至关重要的. 我们的方法最大限度地减少了医学生的输入提示令牌,大大降低了成本,提高了效率.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医学教育 医学教育
背景情况:
- 人们越来越担心来自大型语言模型 (LLM) 推断的碳排放.
- 在医学教育等专业领域需要高效的快速工程.
- 当前提示符长度可能是资源密集的.
研究的目的:
- 开发一种方法来最大限度地减少LLM的输入提示符计数.
- 减少医学教育中LLM使用对环境的影响.
- 为了提高医学学生的LLM互动的效率.
主要方法:
- 使用英语体检课程材料.
- 应用翻译和转述技术来提示.
- 基线和优化提示的比较令牌计数.
主要成果:
- 英国基线提示被发现比韩国提示更少的令牌.
- 与基线提示相比,拟议的转述提示显著减少了令牌计数.
- 在LLM提示符中展示了用于令牌优化的方法.
结论:
- 快速工程可以有效地减少LLMs的碳足迹.
- 释是一种可行的策略,用于创建代币效率高的提示.
- 优化提示对医疗教育中的环境可持续性和用户效率都有好处.
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