Sustainable NLP: An Analysis of Efficient Language Processing Methods

This research investigates methods for developing environmentally sustainable natural language processing systems, focusing on reducing computational costs and energy consumption.

The study analyzes various efficiency techniques specific to NLP tasks, including model compression, efficient attention mechanisms, and task-specific optimizations.

The authors provide empirical evidence of energy savings and performance trade-offs across different NLP tasks and model architectures.