In this work, we introduce the concept of complex text style transfer tasks,
and constructed complex text datasets based on two widely applicable scenarios.
Our dataset is the first large-scale data set of its kind, with 700 rephrased
sentences and 1,000 sentences from the game Genshin Impact. While large
language models (LLM) have shown promise in complex text style transfer, they
have drawbacks such as data privacy concerns, network instability, and high
deployment costs. To address these issues, we explore the effectiveness of
small models (less than T5-3B) with implicit style pre-training through
contrastive learning. We also propose a method for automated evaluation of text
generation quality based on alignment with human evaluations using ChatGPT.
Finally, we compare our approach with existing methods and show that our model
achieves state-of-art performances of few-shot text style transfer models