Abstract
Detecting AI-generated text, especially in short-context documents, is difficult because there is not enough context for accurate classification. This paper presents a new teacher-student model that uses domain adaptation and data augmentation to solve these problems. The teacher model, which combines DeBERTa-v3-large and Mamba-790m, learns semantic knowledge through domain-specific fine-tuning. The student model handles short-context text more efficiently. The system uses a Mean Squared Error (MSE) loss function to guide the student's learning, improving both accuracy and efficiency. Also, data augmentation methods like spelling correction and error injection make the model more robust. Experimental results show that this approach works better than baseline methods, proving its usefulness for real-time AI-generated text detection and other text classification tasks.
Abstract (translated)
检测AI生成文本，尤其是在短文档中，由于缺乏足够的上下文信息来进行准确分类而变得困难。本文介绍了一种新的师生模型，该模型结合领域适应和数据增强技术来解决这些问题。教师模型融合了DeBERTa-v3-large和Mamba-790m，并通过特定领域的微调学习语义知识。学生模型则更高效地处理短文本上下文的检测任务。系统采用均方误差（Mean Squared Error，MSE）损失函数引导学生的训练过程，从而提高准确性和效率。此外，数据增强方法如拼写纠错和错误注入使模型更加健壮。实验结果表明，该方法比基线方法表现更好，证明了其在实时AI生成文本检测及其他文本分类任务中的实用性。
URL
https://arxiv.org/abs/2501.14288
