CAMO is an ensemble technique that dynamically improves minority class predictions in imbalanced language model evaluations and achieves the highest macro F1 scores on two domain-specific benchmarks.
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CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
CAMO is an ensemble technique that dynamically improves minority class predictions in imbalanced language model evaluations and achieves the highest macro F1 scores on two domain-specific benchmarks.