  Large language models trained primarily in a monolingual setting have
demonstrated their ability to generalize to machine translation using zero- and
few-shot examples with in-context learning. However, even though zero-shot
translations are relatively good, there remains a discernible gap comparing
their performance with the few-shot setting. In this paper, we investigate the
factors contributing to this gap and find that this gap can largely be closed
(for about 70%) by matching the writing styles of the target corpus.
Additionally, we explore potential approaches to enhance zero-shot baselines
without the need for parallel demonstration examples, providing valuable
insights into how these methods contribute to improving translation metrics.
