AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917543678050304 |
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| author | Thillainathan, Sarubi Lee, Ji-Ung Sullivan, Michael Koller, Alexander |
| author_facet | Thillainathan, Sarubi Lee, Ji-Ung Sullivan, Michael Koller, Alexander |
| contents | 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 in both automatic and human evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23069 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing Thillainathan, Sarubi Lee, Ji-Ung Sullivan, Michael Koller, Alexander Computation and Language Artificial Intelligence 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 in both automatic and human evaluations. |
| title | AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.23069 |