  In this paper, we introduce a data-driven approach for Formality-Sensitive
Machine Translation (FSMT) that caters to the unique linguistic properties of
four target languages. Our methodology centers on two core strategies: 1)
language-specific data handling, and 2) synthetic data generation using
large-scale language models and empirical prompt engineering. This approach
demonstrates a considerable improvement over the baseline, highlighting the
effectiveness of data-centric techniques. Our prompt engineering strategy
further improves performance by producing superior synthetic translation
examples.

Computation and Language;Artificial Intelligence