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

• URL: http://arxiv.org/abs/2205.11503v1
• Date: Mon, 23 May 2022 17:57:15 GMT
• Title: Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models
• Authors: Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
• Abstract summary: We propose a method for arbitrary textual style transfer (TST) Our method, Prompt-and-Rerank, is based on a mathematical formulation of the TST task. Empirically, our method enables small pre-trained language models to perform on par with state-of-the-art large-scale models.
• Score: 27.454582992694974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models. Our method, Prompt-and-Rerank, is based on a mathematical formulation of the TST task, decomposing it into three constituent components: textual similarity, target style strength, and fluency. Specifically, our method first uses zero-shot or few-shot prompting to obtain a set of candidate generations in the target style, and then re-ranks these candidates according to a combination of the three components above. Empirically, our method enables small pre-trained language models to perform on par with state-of-the-art large-scale models while consuming two orders of magnitude less compute and memory. Finally, we conduct a systematic investigation of the effect of model size and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) on style transfer quality across seven diverse textual style transfer datasets.

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