Jiaqi Chen^*, Xiaoye Zhu^*, Tianyang Liu^*, Ying Chen, Xinhui Chen,
Yiwen Yuan, Chak Tou Leong, Zuchao Li^†, Tang Long, Lei Zhang,
Chenyu Yan, Guanghao Mei, Jie Zhang^†, Lefei Zhang^†

*Equal contribution.
†Equal contribution of corresponding author.

Detecting machine-revised text remains a challenging task as it often involves subtle style changes embedded within human-originated content. The ImBD framework introduces a novel approach to tackle this problem, leveraging style preference optimization (SPO) and Style-CPC to effectively capture machine-style phrasing. Our method achieves state-of-the-art performance in detecting revisions by open-source and proprietary LLMs like GPT-3.5 and GPT-4o, demonstrating significant efficiency with minimal training data.

We are excited to share our code and data to support further exploration in detecting machine-revised text. We welcome your feedback and invite collaborations to advance this field together!