In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which frames style transfer as a sentence rewriting task and requires only a natural language instruction, without model fine-tuning or exemplars in the target style. Augmented zero-shot learning is simple and demonstrates promising results not just on standard style transfer tasks such as sentiment, but also on arbitrary transformations such as "make this melodramatic" or "insert a metaphor."

翻译：在本文中, 我们利用大型语言模型( LMs) 来进行零光文本样式传输。 我们展示了一种提示性方法, 我们称之为“ 强化零光学习 ”, 将样式转换作为重写句子的任务, 只需要自然语言教学, 而不需要在目标样式中进行模型微调或演示。 强化零光学习很简单, 并展示了有希望的结果, 不仅在标准风格转换任务上, 比如情绪, 而且在任意转换上, 比如“ 将这种模范化” 或“ 插入一个隐喻 ” 。