用少量样本实现精准作者风格迁移，兼顾风格还原与语义不变。
HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks
- 通过上下文感知的风格导航选择目标风格坐标
- 采用动态参数调制实现风格迁移，比传统方法快1.8倍以上
- 仅需2.4%额外参数，跨领域迁移能力强，适合低资源场景
低资源作者风格迁移（LAST）旨在仅用少量参考文本，将原文改写为目标作者风格的同时保持原意。现有方法常因将多样参考压缩为单一静态作者嵌入而丢失上下文相关的风格差异，且依赖隐状态控制风格，导致风格与内容混淆。本文提出HyperStyler，将风格迁移解耦为风格选择与风格实现两步：风格导航器联合建模源上下文与目标作者参考，预测风格坐标；风格超网络通过动态参数调制实现风格，而非注入隐藏状态。在Reddit、Blog和News数据集上的实验表明，HyperStyler持续优于先前方法，包括基于大模型的方法，并在跨领域上表现出强泛化能力。值得注意的是，其仅比T5-large多出2.4%参数，推理速度超过1.8倍于大模型。
原文摘要 · Abstract (English)
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 style realization. Stylo-navigator predicts style coordinates by jointly modeling the source context and target-author references, and Stylo-hypernet realizes them via dynamic parameter modulation instead of hidden-state injection. Our experiments on Reddit, Blog, and News datasets demonstrate that HyperStyler consistently outperforms prior methods including LLM-based approaches and generalizes robustly across domains. Notably, HyperStyler achieves superior performance with as few as 2.4% additional parameters over T5-large, while being over 1.8x faster than LLMs at inference.
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