大型语言模型供应链中的可持续性 - - 使用对影响因素有用性的分析进行洞察和建议
Vinaytosh Mishra1,2, Deepika Saxena3, Kishu Gupta4
1Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, 442107, India. dr.vinaytosh@gmu.ac.ae.
Scientific reports
|September 29, 2025
概括
大型语言模型 (LLM) 供应链的可持续性至关重要. 数据质量和伦理考虑是最重要的,而环境影响意识较低,需要人工智能组织进行战略改进.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 供应链管理 供应链管理
背景情况:
- 大型语言模型 (LLM) 的日益普及引发了对供应链可持续性的担忧,包括能源使用,资源需求和碳足迹.
- 当前的LLM供应链在平衡计算需求与环境和道德责任方面面临着挑战.
研究的目的:
- 系统地识别和优先考虑影响大型语言模型供应链可持续性的关键因素.
- 制定一个框架,以提高LLM供应链的可持续性,特别是在资源有限的环境中.
主要方法:
- 一种两步的方法,将Delphi方法用于因素识别和联合分析用于因素优先级.
- 通过Delphi方法进行四轮专家反,确定了关键的可持续性因素.
- 联合分析量化了确定因素的相对重要性.
主要成果:
- 确定的关键因素包括环境影响,计算效率和资源优化,数据质量和道德考虑,以及社会责任和治理.
- 环境影响的优先级得分很低,这表明潜在的意识不足.
- 数据质量和道德考虑成为受访者最重要的因素.
结论:
- 德尔菲联合分析方法有效地优先考虑了LLM供应链可持续性因素.
- 为人工智能组织提供可操作的见解,以提高其LLM运营的可持续性.
- 调查结果强调需要提高对LLM供应链对环境影响的认识和战略关注.
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