STADEE: STAtistics-based DEEp Detection of Machine Generated Text
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2023
- DOI:
- 10.48550/arXiv.2312.01672
- arXiv:
- arXiv:2312.01672
- Bibcode:
- 2023arXiv231201672C
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence
- E-Print:
- In: Advanced Intelligent Computing Technology and Applications, ICIC 2023, Lecture Notes in Computer Science, vol 14089. Springer, Singapore (2023)