Language Models

This innovative research demonstrates how simple addition operations can be used to create more energy-efficient language models without sacrificing performance. The authors propose a novel architecture that significantly reduces computational complexity and energy consumption while maintaining model capabilities. The study provides empirical evidence showing substantial energy savings compared to traditional transformer architectures.

This comprehensive survey examines various approaches to make the training of large language models more efficient and environmentally sustainable. The research analyzes different techniques including model compression, efficient attention mechanisms, and hardware-aware training strategies that can significantly reduce the computational and energy costs. The authors provide a systematic comparison of different efficiency methods and their impact on model performance, training time, and energy consumption.