  Attribute-controlled translation (ACT) is a subtask of machine translation
that involves controlling stylistic or linguistic attributes (like formality
and gender) of translation outputs. While ACT has garnered attention in recent
years due to its usefulness in real-world applications, progress in the task is
currently limited by dataset availability, since most prior approaches rely on
supervised methods. To address this limitation, we propose Retrieval and
Attribute-Marking enhanced Prompting (RAMP), which leverages large multilingual
language models to perform ACT in few-shot and zero-shot settings. RAMP
improves generation accuracy over the standard prompting approach by (1)
incorporating a semantic similarity retrieval component for selecting similar
in-context examples, and (2) marking in-context examples with attribute
annotations. Our comprehensive experiments show that RAMP is a viable approach
in both zero-shot and few-shot settings.
