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arXiv The work proposes AuthorMix, a lightweight, modular, and interpretable authorship style transfer framework that first trains individual style-specific LoRA adapters on a small set of high-resource authors and then, via reinforcement-learning-based layer-wise adapter mixing, rapidly builds a specialized adaptation model for each new target using only a handful of target-style training examples; according to the abstract, AuthorMix ranks first on the combined style-meaning score among all baselines including GPT-5.1, substantially improves meaning preservation over the trained baselines, and is the only method best-or-tied on every dimension under human evaluation.
The work proposes AuthorMix, a lightweight, modular, and interpretable authorship style transfer framework that first trains individual style-specific LoRA adapters on a small set of high-resource authors and then, via reinforcement-learning-based layer-wise adapter mixing, rapidly builds a specialized adaptation model for each new target using only a handful of target-style training examples; according to the abstract, AuthorMix ranks first on the combined style-meaning score among all baselines including GPT-5.1, substantially improves meaning preservation over the trained baselines, and is the only method best-or-tied on every dimension under human evaluation.
The work proposes AuthorMix, a lightweight, modular, and interpretable authorship style transfer framework that first trains individual style-specific LoRA adapters on a small set of high-resource authors and then, via reinforcement-learning-based layer-wise adapter mixing, rapidly builds a specialized adaptation model for each new target using only a handful of target-style training examples; according to the abstract, AuthorMix ranks first on the combined style-meaning score among all baselines including GPT-5.1, substantially improves meaning preservation over the trained baselines, and is the only method best-or-tied on every dimension under human evaluation.
The work proposes AuthorMix, a lightweight, modular, and interpretable authorship style transfer framework that first trains individual style-specific LoRA adapters on a small set of high-resource authors and then, via reinforcement-learning-based layer-wise adapter mixing, rapidly builds a specialized adaptation model for each new target using only a handful of target-style training examples; according to the abstract, AuthorMix ranks first on the combined style-meaning score among all baselines including GPT-5.1, substantially improves meaning preservation over the trained baselines, and is the only method best-or-tied on every dimension under human evaluation.