arXiv AI
Sep 11

Scored vs. Generated Readouts in Behavioral Language Models: An Empirical Study of Elicitation Format

The study compares two ways of obtaining predictions from language models fine‑tuned on customer behavior: scoring answer tokens directly versus generating a written rationale and then scoring the resulting answer. Across 13 model‑domain cells covering four retail tasks, scored readouts consistently rank outcomes more accurately than generated readouts, with an AUC improvement ranging from 1.5 to 14.5 points. The authors also find that a third readout—eliciting a probability before any verdict—improves calibration but only when outcome rates are represented in training, and they recommend using generated rationales for interpretability while relying on scored heads for ranking.

By Touchapon Kraisingkorn, Krittin Pachtrachai, Wachiravit Modecrua
arXiv Computer Vision
Aug 25

FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling

arXiv:2608.21839v1 Announce Type: new Abstract: Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference effic...

By Peiyuan Zhang, Xiangyu Zhao, Hongbo Liu, Xiaoxing Hu, Mingxin Liu, Shuran Ma, Yunhang Shen, Jian Hu, Haihan Gao, Haoyu Cao, Xue Yang