RAMP：用于属性控制翻译的检索与属性标记增强提示
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
AI总结：
针对属性控制翻译受限于数据集可用性的问题，提出RAMP方法，利用大型多语言模型在少样本和零样本设置下，通过语义相似度检索和属性注释标记上下文示例，有效提升了翻译生成准确性。
摘要：
属性控制翻译（ACT）是机器翻译的一个子任务，涉及控制翻译输出的风格或语言属性（如正式性和性别）。尽管ACT近年来因其在实际应用中的实用性而备受关注，但由于大多数先前的方法依赖监督方法，该任务的进展目前受限于数据集的可用性。为了解决这一限制，我们提出了检索与属性标记增强提示（RAMP），利用大型多语言模型在少样本和零样本设置下执行ACT。RAMP通过（1）引入语义相似度检索组件以选择相似的上下文示例，以及（2）用属性注释标记上下文示例，提高了标准提示方法的生成准确性。我们的综合实验表明，RAMP在零样本和少样本设置下都是一种可行的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
