HS-Brexit

Ensemble Diversity Optimization (EDO) improves calibration on subjective NLP tasks

Researchers introduce Ensemble Diversity Optimization (EDO), a differentiable framework that jointly optimizes ensemble weights, effective cardinality, and calibration for subjective NLP tasks where annotator disagreement is systematic. EDO uses Gumbel-Softmax relaxation and a signed diversity regularizer to prevent ensemble collapse and navigate the utility-calibration trade-off. Experiments on four subjective text-classification benchmarks show 40-78% reductions in cross-entropy versus baselines while maintaining competitive F1 and better alignment with annotator distributions.