Prompting pre-trained Large Language Models for formality-controlled En-Ja Translation

Abstract

In this paper, we study the effectiveness of several prompting techniques for controlling the formality level of machine translation (MT) using former existing pre-trained Large Language Models (LLM), including GPT-3 and ChatGPT. Our experimental setting includes a selection of state-of-the-art LLMs and uses an En-Ja parallel corpus specifically designed to test formality control in machine translation, and we propose an approach based on machine learning for evaluating the control capabilities of MT models. Overall, our results provide empirical evidence suggesting that our classification-based evaluation works well in practice and that prompting is a viable approach to control the formality level of En-Ja machine translation using LLMs.