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.

本文介绍了一种基于数据驱动的方法用于形式感知机器翻译，包括语言特定的数据处理、使用大规模语言模型和实证提示工程产生合成数据等核心策略，该方法在改进了基础框架的基础上获得了显著的改进，突出了数据中心技术的有效性。提示工程策略通过生成优秀的合成翻译示例进一步提高了模型的性能。