UO-FIE: Combining Exact-Label Supervision with Graded Utility for Factivity Inference

UO-FIE, a parameter-efficient system built on Qwen3.5-9B with LoRA, ranked first in the fine-tuning track of the Factivity Inference Evaluation 2026 (FIE2026) with a macro utility of 0.8316, according to the paper's abstract. The system combines hard-label supervision, utility-based soft targets, scheduled class weights, and an ordinal loss to predict a distribution over nine ordered factivity intervals for Chinese context-hypothesis pairs, addressing a training set where 64.1% of 566 examples fall in a single class. A separate prompt-based ensemble ranked third in the non-fine-tuning track with a macro utility of 0.8450.

arXiv:2609.28605v1 Announce Type: new Abstract: The Factivity Inference Evaluation 2026 FIE2026 classifies Chinese context-hypothesis pairs into nine ordered factivity intervals. Its evaluation metric rewards both exact predictions and proximity to the correct interval, while 64.1% of the 566 training examples belong to a single class. In preliminary experiments, several mDeBERTa classification models predominantly predict the dominant class, whereas a Huber-regression baseline produces more predictions near the correct interval but fewer exact matches. We introduce Utility-Oriented Factivity Inference UO-FIE , a parameter-efficient system that combines exact-label supervision with graded utility. UO-FIE predicts a distribution over the nine classes and combines hard-label supervision, utility-based soft targets, scheduled class weights, and an ordinal loss. We evaluate expected-utility decoding in controlled comparisons and use ordinal calibration selected on out-of-fold predictions for the submitted system. Based on Qwen3.5-9B with LoRA, UO-FIE ranks first in the fine-tuning track with a macro utility of 0.8316. A separate prompt-based ensemble ranks third in the non-fine-tuning track with a macro utility of 0.8450.