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.
MSA at BEA 2025 Shared Task: Disagreement-Aware Instruction Tuning for Multi-Dimensional Evaluation of LLM s as Math Tutors
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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.