Abstract
Large Language Models (LLMs) have demonstrated strong capabilities in rewriting text across various styles. However, effectively leveraging this ability for example-based arbitrary style transfer, where an input text is rewritten to match the style of a given exemplar, remains an open challenge. A key question is how to describe the style of the exemplar to guide LLMs toward high-quality rewrites. In this work, we propose a prompting method based on register analysis to guide LLMs to perform this task. Empirical evaluations across multiple style transfer tasks show that our prompting approach enhances style transfer strength while preserving meaning more effectively than existing prompting strategies.
Abstract (translated)
大型语言模型（LLMs）在跨各种风格改写文本方面表现出强大的能力。然而，如何有效利用这种能力进行基于示例的任意风格转换仍是一个开放性挑战，尤其是在将输入文本改写为与给定示例相匹配的风格时。关键问题是应如何描述示例的风格以引导LLMs生成高质量的重写内容。在本研究中，我们提出了一种基于语域分析的提示方法来指导LLMs完成这一任务。通过多项风格转换任务的实际评估表明，我们的提示策略相比现有的提示策略能够更有效地增强风格转换能力同时更好地保留原意。
URL
https://arxiv.org/abs/2505.00679
