Evaluating the Environmental Impact of Large Language Models: Sustainable Approaches and Practices

Abstract

Large language models (LLMs) have revolutionized natural language processing (NLP) but have also raised concerns about their environmental impact. This paper evaluates various approaches and practices aimed at mitigating the environmental footprint of LLMs. Firstly, it discusses the energy consumption associated with training and inference phases of LLMs, highlighting the significant computational resources required. Next, it explores sustainable approaches such as optimizing model architecture, improving hardware efficiency, and utilizing renewable energy sources for computing tasks. Furthermore, the paper examines the role of data efficiency and algorithmic improvements in reducing the carbon footprint of LLMs. It also considers the importance of lifecycle assessments and carbon accounting methodologies in accurately measuring and managing environmental impacts across the lifespan of LLMs. Finally, the study emphasizes collaborative efforts among researchers, developers, and policymakers to implement sustainable practices and innovations in the development and deployment of large language models. These efforts are crucial for advancing the field of NLP while ensuring environmental responsibility in technological advancements.