  Large language models (LLMs) are a promising avenue for machine translation
(MT). However, current LLM-based MT systems are brittle: their effectiveness
highly depends on the choice of few-shot examples and they often require extra
post-processing due to overgeneration. Alternatives such as finetuning on
translation instructions are computationally expensive and may weaken
in-context learning capabilities, due to overspecialization. In this paper, we
provide a closer look at this problem. We start by showing that adapter-based
finetuning with LoRA matches the performance of traditional finetuning while
reducing the number of training parameters by a factor of 50. This method also
outperforms few-shot prompting and eliminates the need for post-processing or
in-context examples. However, we show that finetuning generally degrades
few-shot performance, hindering adaptation capabilities. Finally, to obtain the
best of both worlds, we propose a simple approach that incorporates few-shot
examples during finetuning. Experiments on 10 language pairs show that our
proposed approach recovers the original few-shot capabilities while keeping the
added benefits of finetuning.
