  To build large language models for Ukrainian we need to expand our corpora
with large amounts of new algorithmic tasks expressed in natural language.
Examples of task performance expressed in English are abundant, so with a
high-quality translation system our community will be enabled to curate
datasets faster. To aid this goal, we introduce a recipe to build a translation
system using supervised finetuning of a large pretrained language model with a
noisy parallel dataset of 3M pairs of Ukrainian and English sentences followed
by a second phase of training using 17K examples selected by k-fold perplexity
filtering on another dataset of higher quality. Our decoder-only model named
Dragoman beats performance of previous state of the art encoder-decoder models
on the FLORES devtest set.
