A Recipe for Arbitrary Text Style Transfer with Large Language Models

Emily Reif; Daphne Ippolito; Ann Yuan; Andy Coenen; Chris Callison-Burch; Jason Wei

ACL 2022short186 citations

A Recipe for Arbitrary Text Style Transfer with Large Language Models

Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, Jason Wei

Abstract

In this paper, we leverage large language models (LLMs) 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.’

BibTeX

@inproceedings{reif-etal-2022-recipe,
    title = "A Recipe for Arbitrary Text Style Transfer with Large Language Models",
    author = "Reif, Emily  and
      Ippolito, Daphne  and
      Yuan, Ann  and
      Coenen, Andy  and
      Callison-Burch, Chris  and
      Wei, Jason",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.94/",
    doi = "10.18653/v1/2022.acl-short.94",
    pages = "837--848"
}