关键词: 人工智能生成文本检测；生成式大语言模型；支持向量机；对比学习

Abstract: With the rapid development of generative large language models, the texts generated by these models are increasingly similar to human texts. Efficient detection is required to avoid misleading the public. Nevertheless, sophisticated and accurate detection methods suffer from high computational resource consumption and long inference time. To meet this requirement, this paper designs a lightweight detection scheme. Specifically, Sentence-BERT is adopted to capture implicit semantic logical correlations of texts; the Natural Language Toolkit (NLTK) library is used to extract sentiment and statistical features for constructing a shallow feature set; an attention-weighted mechanism is introduced for adaptive fusion of the two categories of shallow features to strengthen the representation capability of critical shallow information; after dimension-wise concatenation of fused shallow features and deep semantic representations, contrastive learning is utilized to optimize joint features, and Support Vector Machine (SVM) is applied for final classification. The model adopts a coupled architecture of deep semantic representation and lightweight classification, which fundamentally reduces computational resource consumption and shortens inference time. It achieves detection accuracies of 92% and 90% on Chinese and English test sets, realizing the balance between detection accuracy and efficiency.

Key words: AI-generated text detection; generative large language model; support vector machine; contrastive learning