Green Training of Large Language Models: Challenges and Techniques
This research investigates techniques for making the training of large language models more environmentally sustainable without compromising model performance. The authors propose novel methods for reducing energy consumption during training, including adaptive batch sizing, efficient model architectures, and intelligent resource allocation. The study provides extensive empirical analysis of different training strategies and their impact on both model quality and environmental footprint.