CAT-LLM: Prompting Large Language Models with Text Style Definition for
  Chinese Article-style Transfer
Zhen Tao Dinghao Xi Zhiyu Li Liumin Tang Wei Xu
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.