Foundation language models learn from their finetuning input context in different ways. In this paper, we reformulate inputs during finetuning for challenging translation tasks, leveraging model strengths from pretraining in novel ways to improve downstream performance. These reformulations are simple data level modifications, require no additional collection of training data or modification of data at inference time. They can be applied either on single language pair translation tasks or massively multilingual translation tasks. Experiments with these techniques demonstrate significant performance improvements up to $\textbf{3.5 chrF++ on the Flores200 translation benchmark}$. We hope our research accessibly improves finetuning data efficiency, enabling more effective training to scalably improve state-of-the-art performance. Our code is released $\href{https://github.com/bri25yu/LanguageModelExperimentation}{here}.$

通过重新制定微调期间的输入，利用预训练模型在新颖的方式下发挥其优势，无需额外收集训练数据或在推理时修改数据，这些简单的数据级别修改方案在单语言对翻译任务或大规模多语言翻译任务中都可以应用，实验证明这些技术在Flores200翻译基准测试中实现了显著的性能提升达到3.5 chrF++。我们希望通过提高微调数据效率的可访问性，使训练更加有效，以达到可扩展改进的最新性能水平。我们的代码在此处发布

翻译的简单有效的输入改写