  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.
