When Do External Priors Help Low-Resource Structured Prediction? A Ranking-Level Alignment Study
The Ranking Prior Alignment framework distills external ranking knowledge into any scoring model using a temperature-scaled KL divergence loss. It unifies neural and tree-based architectures for cold-start credit scoring by aligning model predictions with expert or teacher rankings. The method shows that alignment gains scale inversely with data abundance, model capacity, and feature quality.