@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",
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.{'}",
}
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<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.’</abstract>
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%0 Conference Proceedings
%T A Recipe for Arbitrary Text Style Transfer with Large Language Models
%A Reif, Emily
%A Ippolito, Daphne
%A Yuan, Ann
%A Coenen, Andy
%A Callison-Burch, Chris
%A Wei, Jason
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F reif-etal-2022-recipe
%X 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.’
%R 10.18653/v1/2022.acl-short.94
%U https://aclanthology.org/2022.acl-short.94
%U https://doi.org/10.18653/v1/2022.acl-short.94
%P 837-848
Markdown (Informal)
[A Recipe for Arbitrary Text Style Transfer with Large Language Models](https://aclanthology.org/2022.acl-short.94) (Reif et al., ACL 2022)
ACL
- Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, and Jason Wei. 2022. A Recipe for Arbitrary Text Style Transfer with Large Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 837–848, Dublin, Ireland. Association for Computational Linguistics.