Abstract
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.
Abstract (translated)
作者风格迁移的任务旨在在保持原文语义的前提下，将文本重写为目标作者的风格。现有风格迁移方法通常在大规模语料上训练单一模型以同时建模所有目标风格：这种高成本方法对目标特定适应的灵活性有限，且常以牺牲语义保持为代价来实现风格迁移。本文提出AuthorMix：一个轻量、模块化且可解释的风格迁移框架。我们在少量高资源作者的数据上训练独立的、风格特定的LoRA适配器，通过学习的逐层适配器混合，仅需少量目标风格训练样本即可快速训练针对每个新目标的专用适应模型。AuthorMix在低资源目标上超越了现有最先进风格迁移基线以及GPT-5.1，取得了最高总分并显著提升了语义保持效果。
URL
https://arxiv.org/abs/2603.23069
