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Chinese Academy of Sciences, University of Chinese Academy of Sciences
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EVIL-Detect outperforms existing methods by achieving a macro-F1 score of 0.8888 in detecting LLM-generated text, even under challenging conditions.
A novel ensemble method substantially improves the reliability of detecting Chinese LLM-generated text, even against adversarial examples.
Finally, a practical way to audit LLM watermarks without needing the model provider's secret sauce.
