When Do External Priors Help Low-Resource Structured Prediction? A Ranking-Level Alignment Study
Read the original on arXiv Machine Learning →The paper investigates when external priors (teacher models, LLMs, or domain experts) help low‑resource structured prediction, introducing Ranking Prior Alignment (RPA) as a model‑agnostic objective that aligns a model’s ranking distribution to an external prior via a temperature‑scaled KL loss. Experiments on tree‑ensemble and multiple‑instance‑learning tasks show that a prior’s benefit depends on its quality (rank correlation with labels) and the granularity of alignment: high‑quality priors improve performance in cross‑sample tree alignment but can hurt within‑bag MIL alignment, while zero‑information priors act as entropy regularization. The authors propose a two‑factor decision rule and a protocol to separate information transfer from regularization.
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