AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing
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 first train individual, style-specific LoRA adapters on a small set of high-resource authors: this allows for the rapid training of specialized adaptation models for each new target using layer...
| Authors | Sarubi Thillainathan, Ji-Ung Lee, Michael Sullivan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 16 September 2026 |
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