Text style transfer is increasingly prominent in online entertainment and social media. However, existing research mainly concentrates on style transfer within individual English sentences, while ignoring the complexity of long Chinese texts, which limits the wider applicability of style transfer in digital media realm. To bridge this gap, we propose a Chinese Article-style Transfer framework (CAT-LLM), leveraging the capabilities of Large Language Models (LLMs). CAT-LLM incorporates a bespoke, pluggable Text Style Definition (TSD) module aimed at comprehensively analyzing text features in articles, prompting LLMs to efficiently transfer Chinese article-style. The TSD module integrates a series of machine learning algorithms to analyze article-style from both words and sentences levels, thereby aiding LLMs thoroughly grasp the target style without compromising the integrity of the original text. In addition, this module supports dynamic expansion of internal style trees, showcasing robust compatibility and allowing flexible optimization in subsequent research. Moreover, we select five Chinese articles with distinct styles and create five parallel datasets using ChatGPT, enhancing the models' performance evaluation accuracy and establishing a novel paradigm for evaluating subsequent research on article-style transfer. Extensive experimental results affirm that CAT-LLM outperforms current research in terms of transfer accuracy and content preservation, and has remarkable applicability to various types of LLMs.

翻译：文本风格迁移在网络娱乐和社交媒体中日益重要。然而，现有研究主要聚焦于英文单句内的风格迁移，忽视了中文长文本的复杂性，这限制了风格迁移在数字媒体领域的广泛适用性。为弥补这一不足，我们提出了一种中文文章风格迁移框架（CAT-LLM），借力大语言模型的能力。CAT-LLM集成了一个定制化、可插拔的文本风格定义模块，旨在全面分析文章的文本特征，引导大语言模型高效完成中文文章风格迁移。该模块整合了一系列机器学习算法，从词汇和句子两个层面分析文章风格，从而帮助大语言模型在不破坏原文完整性的前提下充分把握目标风格。此外，该模块支持内部风格树的动态扩展，展现出强大的兼容性，便于后续研究进行灵活优化。同时，我们选取了五篇风格迥异的中文文章，并利用ChatGPT创建了五个平行数据集，增强了模型性能评估的准确性，并为文章风格迁移的后续研究建立了新的评估范式。大量实验结果表明，CAT-LLM在迁移准确性和内容保留方面优于现有研究，并且对各类大语言模型具有显著的适用性。