Artificial Intelligence and Translation: Exploring Current Applications, Limitations and Future Potential of Language Models Through Japanese-English Translation

Abstract

This thesis highlights the recent improvements and capabilities of Large Language Models (LLMs), specifically their ability to produce translations between different languages. The continued up-scaling of model sizes has led to breakthroughs in the level of their observed intelligence, allowing them to produce translations that are similar in quality to highly skilled human translators. However, to facilitate the reasoning processes that LLMs now possess, their demand for computational power and the supporting hardware and resources has increased proportionally. Considering the impacts of this technology on the environment, energy resources, and its accessibility, my research explores the possibilities of smaller, highly trained models that can run on low-level consumer hardware while still producing quality translations between languages. Through experimenting with ten small models running locally on my own desktop’s hardware, I produced, evaluated, and analyzed the generated translations created by prompting the models on Japanese to English texts in the contexts of fictional prose, historical writing, and spoken conversation. I conclude my thesis by noting that current use cases highly depend on the severity and context of the situation in which translations are being produced, but that the future of language models supporting accessible and quality translations is optimistic.