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HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target author using only a few reference examples while preserving the original meaning. Existing methods often struggle to achieve both high style fidelity and semantic preservation because they compress diverse references into a single static author embedding, which averages out context-dependent stylistic variation, and rely on hidden representations for style control, which entangle style with content. We propose HyperStyler, a novel architecture that decouples LAST into style selection and st

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