为什么单凭生成式大型语言模型的碳足迹不能帮助我们评估它们的可持续性

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2025-02-03 DOI:10.1038/s42256-025-00979-y

Leonie N. Bossert, Wulf Loh

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引用次数: 0

摘要

人们越来越意识到大型语言模型（llm）的巨大环境成本，但仅从二氧化碳排放的角度讨论llm的可持续性是不够的。本意见强调也需要考虑法学硕士的社会和生态成本和效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Why the carbon footprint of generative large language models alone will not help us assess their sustainability

There is a growing awareness of the substantial environmental costs of large language models (LLMs), but discussing the sustainability of LLMs only in terms of CO2 emissions is not enough. This Comment emphasizes the need to take into account the social and ecological costs and benefits of LLMs as well.

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来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.