We present STADEE, a \textbf{STA}tistics-based \textbf{DEE}p detection method
to identify machine-generated text, addressing the limitations of current
methods that rely heavily on fine-tuning pre-trained language models (PLMs).
STADEE integrates key statistical text features with a deep classifier,
focusing on aspects like token probability and cumulative probability, crucial
for handling nucleus sampling. Tested across diverse datasets and scenarios
(in-domain, out-of-domain, and in-the-wild), STADEE demonstrates superior
performance, achieving an 87.05% F1 score in-domain and outperforming both
traditional statistical methods and fine-tuned PLMs, especially in
out-of-domain and in-the-wild settings, highlighting its effectiveness and
generalizability.

我们介绍了 STADEE，一种基于统计数据的深度检测方法，用于识别机器生成的文本，解决了当前方法在依赖大量微调预训练语言模型方面的局限性。STADEE 将关键的统计文本特征与深度分类器相结合，重点关注诸如标记概率和累积概率等方面，这对于处理核心采样非常关键。在不同的数据集和场景（领域内、领域外和实际环境）中进行测试，STADEE 表现出优异的性能，在领域内达到了 87.05% 的 F1 得分，并且在传统统计方法和微调预训练语言模型方面都表现出色，特别是在领域外和实际环境中，突显了其有效性和普适性。