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The document presents the N-GRAM COVERAGE ATTACK, a novel membership inference attack that operates solely on text outputs from language models, enabling it to work with black-box models like GPT-4. This method measures the similarity between model-generated outputs and the original text to infer membership, demonstrating superior performance compared to existing black-box methods and comparable to white-box attacks. The findings suggest that newer models exhibit improved robustness against such attacks, in

Author

JWHC CORUMANA PROJECT

Language

EN