RAMP : Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation (2023)

Abstract: 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.

## conclusions we introduced the new ramp in-context learning approach to leverage attribute annotations and similar same-language or cross-lingual examples for better prompting quality. we demonstrated its effectiveness with multilingual llms for both formalitycontrolled and gender-controlled translation. we use gold annotations for attribute marking, but we leave unsupervised automatic attribute span extraction as future work.