license: apache-2.0
language:
- en
metrics:
- roc_auc
base_model:
- EleutherAI/gpt-neo-2.7B
pipeline_tag: text-classification
tags:
- machine-text-detection
- machine-revised-text-detection
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!
