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We propose a method for arbitrary textual style transfer (TST)--the task oftransforming a text into any given style--utilizing general-purpose pre-trainedlanguage models. Our method, Prompt-and-Rerank, is based on a mathematicalformulation of the TST task, decomposing it into three constituent components:textual similarity, target style strength, and fluency. Specifically, ourmethod first uses zero-shot or few-shot prompting to obtain a set of candidategenerations in the target style, and then re-ranks these candidates accordingto a combination of the three components above. Empirically, our method enablessmall pre-trained language models to perform on par with state-of-the-artlarge-scale models while consuming two orders of magnitude less compute andmemory. Finally, we conduct a systematic investigation of the effect of modelsize and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) onstyle transfer quality across seven diverse textual style transfer datasets.

Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models