الملخص
Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet reliability requires more than accuracy: a model must also use the ordinal decision scale supplied by the user faithfully. We analyze JEV~1.13 and three open KEV models. Our investigation begins with ANLI, where JEV assigns 38.8\% of all predictions and 51.3\% of errors to Neutral despite 74.95\% accuracy, nearly balanced gold labels, and balanced candidate positions. Across 36 ordinal datasets, final decisions use only 67--76\% of the effective gold support, versus 87--102\% on four nominal tasks. Randomizing candidate order weakens but does not remove this compression. Holding items and source scores fixed while balancing gold support and positions, we refine scales from $K=2$ to $14$; utilization falls for every model and reaches 26--75\% at $K=14$, although candidate probabilities remain broad for most models. Targeted BA-LoRA post-training raises gold-relative utilization from roughly 47\% to 86\% on eight supervised scales at both KEV sizes, showing that the compression is learned and modifiable rather than an immutable architectural limit. We call this ordinal scale-utilization bias: decision-stage candidate-space compression distinct from accuracy, gold imbalance, fixed position, and candidate count alone. The code and data are available at https://github.com/Glax147/jev_ordinal_scale_bia
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Gao, T., Li, J., Li, Z., Chang, Y., & Wu, Y. (2026). More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models. https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models
MLA 9
Gao, Tianxiang, et al. "More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models." https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models.
شيكاغو (المؤلف–التاريخ)
Gao, Tianxiang, Jinzhe Li, Zhiyuan Li, Yi Chang, and Yuan Wu. 2026. "More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models." https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models.
هارفارد
Gao, T., Li, J., Li, Z., Chang, Y. and Wu, Y. (2026) 'More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models', Available at: https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models.
فانكوفر
Gao T, Li J, Li Z, Chang Y, Wu Y. More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models. https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models
IEEE
T. Gao, J. Li, Z. Li, Y. Chang, and Y. Wu, "More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models," https://omanscience.com/ar/articles/more-choices-fewer-decisions-ordinal-scale-bias-in-jev-like-direct-decision-models.