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This paper systematically evaluates five imbalance handling methods (RUS, ROS, SMOTE, re-weighting, direct F1 optimization) on three biomedical datasets (tabular, text, image) using models of varying complexity. Results show that benefits depend on model complexity and data modality, with ROS, re-weighting, and direct F1 optimization being effective for complex models on unstructured data.
This paper presents a systematic evaluation of how differential privacy impacts social bias in large language models, finding that while it reduces bias in sentence scoring, the effect does not generalize across all tasks.