The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora to model all target styles at once: this high-cost approach offers limited flexibility for target-specific adaptation, and often sacrifices meaning preservation for style transfer. In this paper, we propose AuthorMix: a lightweight, modular, and interpretable style transfer framework. We train individual, style-specific LoRA adapters on a small set of high-resource authors, allowing the rapid training of specialized adaptation models for each new target via learned, layer-wise adapter mixing, using only a handful of target style training examples. AuthorMix outperforms existing, SoTA style-transfer baselines -- as well as GPT-5.1 -- for low-resource targets, achieving the highest overall score and substantially improving meaning preservation.

翻译：作者风格迁移任务旨在将文本重写为目标作者的风格，同时保留原始文本的含义。现有风格迁移方法在大型语料库上训练单一模型以同时建模所有目标风格：这种高成本方法在目标特定的适配方面灵活性有限，且常常为了风格迁移而牺牲意义保留。本文提出AuthorMix：一种轻量级、模块化且可解释的风格迁移框架。我们在少量高资源作者上训练单个风格特定的LoRA适配器，通过学习的、分层适配器混合，仅使用少量目标风格训练示例，即可为每个新目标快速训练专门的适配模型。在低资源目标上，AuthorMix优于现有最先进的风格迁移基线以及GPT-5.1，获得了最高总分并显著提升了意义保留。