Balancing Translation Quality and Environmental Impact: Comparing Large and Small Language Models

Large Language Models (LLMs) have demonstrated remarkable performance in machine translation (MT), specifically concerning high-resource European languages. However, their extensive computational requirements raise sustainability concerns. This paper investigates the potential of smaller, fine-tuned language models as a more sustainable alternative for MT tasks. We conduct a comparative analysis of model performance in terms of translation quality and CO2eq emissions, and examine the key errors associated with using smaller models. Furthermore, we propose a novel metric that balances translation quality against environmental impact, aiming to inform more sustainable model selection in MT research and practice.