More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models

Tianxiang Gao, Jinzhe Li, Zhiyuan Li, Yi Chang, Yuan Wu

Direct-decision models enable efficient classification and evaluation by selecting from predefined candidates, but their reliability depends on more than prediction accuracy. Across 40 datasets and nine models from the JEV, KEV, and StartLux Decision families, we identify a systematic tendency to underuse ordinal rating scales. We call this phenomenon ordinal scale-utilization bias: final decisions concentrate on fewer effective labels than the gold distribution. The bias is substantially weaker on tasks with unordered semantic categories. Evaluating the same items on finer ordinal scales strengthens compression despite controls for gold imbalance and candidate position. Randomizing candidate order reduces but does not eliminate compression, while widening ordinal gaps at fixed candidate count can improve accuracy without restoring utilization. For most models, concentrated decisions coexist with broad average candidate probabilities. Greater model size improves accuracy without consistently restoring scale utilization. Together, these findings characterize compression as strongly associated with ordinal structure and intensified by scale refinement, rather than fully explained…