医学中的大型语言模型对环境的影响
Oliver Kleinig1,2, Shreyans Sinhal1, Rushan Khurram1
1School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.
Internal medicine journal
|November 14, 2024
概括
医学中的大型语言模型 (LLM) 对环境产生了重大影响,包括碳排放和用水. 这项研究为临床研究人员提出了一个五步计划,以减少自然语言算法的生态足迹.
科学领域:
- 环境科学 环境科学
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
背景情况:
- 大型语言模型 (LLM) 提出了相当大的环境挑战,包括大量的碳排放,水消耗和稀有矿物开采.
- 之前的LLM世代如GPT-3展出了关于环境足迹的展览.
- 新兴的LLM,如GPT-4,越来越能源密集,使用频率越来越高,增加了潜在的环境风险.
研究的目的:
- 为解决与医学研究中使用大型语言模型相关的日益严重的环境问题.
- 为临床研究人员提供一个实用的框架,以减轻开发和部署自然语言算法的生态影响.
- 促进医疗人工智能领域的可持续实践.
主要方法:
- 在临床研究背景下对当前和下一代LLM对环境的影响进行全面审查.
- 开发了一种新的五步之路,旨在指导研究人员尽量减少其自然语言处理工具的环境足迹.
- 分析与LLM开发和应用相关的关键环境指标,包括碳排放,用水和资源枯竭.
主要成果:
- 量化与医学LLM相关的重大环境成本,包括碳排放,用水和稀有矿物需求.
- 确定下一代LLM特别能源密集型,对环境构成威胁.
- 拟议的五步之路为减少生态影响提供了可行的策略.
结论:
- 在医学中广泛采用LLM,迫切需要关注其对环境的影响.
- 临床研究人员可以通过结构化,主动的方法来积极减少自然语言算法的环境危害.
- 实施拟议的途径对于促进医疗AI中对环境负责的创新至关重要.
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