xyzhu1225/ImBD

Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection

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!

🔥 News

• [2024, Dec 16] Our online demo is available on hugging-face now!
• [2024, Dec 13] Our model and local inference code are available.
• [2024, Dec 9] 🎉🎉 Our paper has been accepted by AAAI 25!
• [2024, Dec 7] We've released our website.