使用可解释风格计量特征检测GPT辅助写作
Detecting GPT-Assisted Writing Using Interpretable Stylometric Features
- Bucknell University（巴克内尔大学）
- Susquehanna University（萨斯奎汉纳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用可解释风格计量特征和八种分类器检测GPT辅助写作，随机森林在留出集上达到0.87的ROC-AUC，证明文本内在特征可有效区分人机写作。
AI中文摘要：
区分GPT辅助写作与学生独立撰写的文本已成为学术界的一项关键挑战。本文评估了仅从提交文本中提取的可解释风格计量特征的判别能力。利用90名参与者既独立写作又借助ChatGPT辅助写作的数据，我们在验证过程中保持同一参与者的数据不分离，评估了八种机器学习分类器。在留出测试集上，随机森林实现了0.87的ROC-AUC和0.84的F1分数，假阳性率和假阴性率分别为22.2%和11.1%。SHAP分析表明，词汇和语法特征驱动了最终的预测结果。研究结果表明，透明的、文本内在的特征为检测GPT辅助写作提供了可测量的信号。
英文摘要：
Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who wrote both independently and with ChatGPT assistance, we evaluate eight machine learning classifiers while keeping data from the same participant together during validation. On the held-out test set, Random Forest achieved an ROC-AUC of 0.87 and an F1-score of 0.84, with False Positive and False Negative rates of 22.2% and 11.1%, respectively. SHAP analysis shows that lexical and grammatical characteristics drive the resulting predictions. The findings suggest that transparent, text-intrinsic features provide measurable signal for detecting GPT-assisted writing.
