Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training

arXiv:2404.01157v1 Announce Type: new
Abstract: Prominent works in the field of Natural Language Processing have long attempted to create new innovative models by improving upon previous model training approaches, altering model architecture, and developing more in-depth datasets to better their performance. However, with the quickly advancing field of NLP comes increased greenhouse gas emissions, posing concerns over the environmental damage caused by training LLMs. Gaining a comprehensive understanding of the various costs, particularly those pertaining to environmental aspects, that are …

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