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We present STADEE, a \textbf{STA}tistics-based \textbf{DEE}p detection methodto identify machine-generated text, addressing the limitations of currentmethods 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, crucialfor handling nucleus sampling. Tested across diverse datasets and scenarios(in-domain, out-of-domain, and in-the-wild), STADEE demonstrates superiorperformance, achieving an 87.05% F1 score in-domain and outperforming bothtraditional statistical methods and fine-tuned PLMs, especially inout-of-domain and in-the-wild settings, highlighting its effectiveness andgeneralizability.

STADEE: STAtistics-based DEEp Detection of Machine Generated Text