ordinal-classification

UO-FIE is a parameter-efficient system for factivity inference that combines exact-label supervision with graded utility, achieving first place in the fine-tuning track of FIE2026 with a macro utility of 0.8316.

This paper proposes Adaptive Margin Ordinal Loss (AMOL) to address center-class hedging in ordinal classification tasks, achieving improved performance over existing methods.

Introduces a conformal prediction method for ordinal classification using the ranked probability score as a nonconformity function, producing median-centered contiguous prediction sets and achieving favorable balance between set width and ordinal miscoverage.