Feeding four standard membership-inference scores into an XGBoost classifier yields higher AUC-ROC than the individual attacks on seven datasets for LLMs from 160M to 12B parameters.
Yu et al., ”Rotation-invariant transformer for point cloud matching,” in Proc
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EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling
Feeding four standard membership-inference scores into an XGBoost classifier yields higher AUC-ROC than the individual attacks on seven datasets for LLMs from 160M to 12B parameters.