多语言模型在阿拉伯语伪文本检测中表现优于专用模型。
BUSTED at AraGenEval Shared Task: A Comparative Study of Transformer-Based Models for Arabic AI-Generated Text Detection
- 用三种Transformer模型在阿拉伯语数据上微调分类
- XLM-RoBERTa达F1 0.7701，胜过专用模型
- 适合关注跨语言通用性的AI安全研究者
本文介绍我们参加AraGenEval共享任务的成果，团队BUSTED在阿拉伯语AI生成文本检测中获得第5名。我们评估了三种预训练Transformer模型：AraELECTRA、CAMeLBERT和XLM-RoBERTa。通过在给定数据集上对每种模型进行微调，完成二分类任务。结果出人意料：多语言模型XLM-RoBERTa取得最高性能，F1分数达0.7701，优于专为阿拉伯语设计的AraELECTRA与CAMeLBERT。该研究揭示了AI生成文本检测的复杂性，并凸显了多语言模型的强大泛化能力。
原文摘要 · Abstract (English)
This paper details our submission to the AraGenEval Shared Task on Arabic AI-generated text detection, where our team, BUSTED, secured 5th place. We investigated the effectiveness of three pre-trained transformer models: AraELECTRA, CAMeLBERT, and XLM-RoBERTa. Our approach involved fine-tuning each model on the provided dataset for a binary classification task. Our findings revealed a surprising result: the multilingual XLM-RoBERTa model achieved the highest performance with an F1 score of 0.7701, outperforming the specialized Arabic models. This work underscores the complexities of AI-generated text detection and highlights the strong generalization capabilities of multilingual models.
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