发表机构
UNIST; POSTECH; POSCO Holdings Inc.（蔚山国家科学技术研究所; 浦项科技大学; 浦项控股公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HyperStyler通过解耦风格选择与实现的架构，在低资源作者风格迁移任务中，以仅增2.4%参数的规模实现优于现有方法的性能，且推理速度比LLM快1.8倍以上。
AI 中文摘要
低资源作者风格迁移（LAST）旨在仅使用少量参考示例将文本重写为任意目标作者的风格，同时保留原始含义。现有方法常难以同时实现高风格保真度与语义保留，原因在于它们将多样化参考压缩为单个静态作者嵌入，该嵌入会抹平依赖上下文的风格变化，且依赖隐藏表示进行风格控制，这会将风格与内容纠缠在一起。我们提出HyperStyler，一种将LAST解耦为风格选择与风格实现的新型架构。Stylo-navigator（风格导航器）通过联合建模源上下文与目标作者参考来预测风格坐标，Stylo-hypernet（风格超网络）则通过动态参数调制而非隐藏状态注入来实现这些风格坐标。我们在Reddit、Blog和News数据集上开展的实验表明，HyperStyler始终优于包括基于大语言模型（LLM）的方法在内的现有方法，且能跨领域稳健泛化。值得注意的是，HyperStyler仅比T5-large多2.4%的参数即可实现更优性能，推理速度比LLM快1.8倍以上。
英文摘要
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.
CommentsAccepted to EMNLP 2026 (Main)
