  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."
