用语域分析引导大模型实现任意风格迁移，效果更优。
Steering Large Language Models with Register Analysis for Arbitrary Style Transfer
- 基于语域分析设计提示词，精准描述文本风格
- 多任务实验表明风格迁移更强，语义保留更好
- 适合需要高质量风格转换的文本生成场景
大型语言模型（LLMs）在多种文本风格重写任务中表现出强大能力。然而，如何有效利用这一能力实现基于样例的任意风格迁移——即让输入文本重写为匹配给定样例风格——仍是开放挑战。核心问题在于如何描述样例的风格以指导模型生成高质量重写结果。本文提出一种基于语域分析的提示方法，用于引导LLM完成该任务。在多个风格迁移任务上的实证评估显示，该提示方法在增强风格迁移强度的同时，比现有策略更有效地保持原文语义。
原文摘要 · Abstract (English)
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.
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