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A Recipe For Arbitrary Text Style Transfer With Large Language Models

Emily Reif et al.. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2021 – 28 citations

[Paper]    
Training Techniques Fine-Tuning Prompting RAG

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.”

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