语言模型训练的环境影响持续上升，现在是关注反弹效应的时候

Computer Science > Machine Learning arXiv:2510.09022 (cs) [Submitted on 10 Oct 2025 (v1), last revised 17 Sep 2026 (this version, v2)] Title:The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound Authors:Clément Morand (STL), Anne-Laure Ligozat (ENSIIE, LISN, STL), Aurélie Névéol (STL, LISN) View a PDF of the paper titled The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound, by Cl\'ement Morand (STL) and 5 other authors View PDF HTML (experimental) Abstract:Recent Machine Learning (ML) approaches have shown increased performance on benchmarks at the cost of escalating compute demands. Hardware, algorithmic and carbon optimizations have been proposed to curb energy use and environmental impacts. We estimate the environmental impacts associated with training models documented in the Epoch AI database over the last decade, with a particular focus on impacts associated with Large Language Models and the hardware used to train them. We find that energy use and environmental impacts associated with training ML models have increased exponentially, even when considering impact reduction strategies such as using less carbon intensive electricity mixes or more efficient hardware. Optimization strategies do not mitigate the impacts induced by model training, suggesting rebound effect. We show that the impacts of hardware must be considered over the entire life cycle rather than the sole use phase in order to avoid impact shifting. Our study demonstrates that increasing efficiency alone does not ensure sustainability. There is an urgent need to systematically integrate environmental impacts in NLP evaluation practices to better inform the community and support the use of impact as a feature in research planning and decision making. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2510.09022 [cs.LG] (or arXiv:2510.09022v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.09022 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), Oct 2026, Budapest, Hungary Submission history From: Clement Morand [view email] [via CCSD proxy] [v1] Fri, 10 Oct 2025 05:49:37 UTC (908 KB) [v2] Thu, 17 Sep 2026 12:29:23 UTC (937 KB) Full-text links: Access Paper: View a PDF of the paper titled The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound, by Cl\'ement Morand (STL) and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2025-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)