Scored vs. Generated Readouts in Behavioral Language Models: An Empirical Study of Elicitation Format
Language models fine-tuned on customer behavior can predict outcomes and generate explanations, but these readouts are often treated as interchangeable. Holding model checkpoint and prompt content fixed, we compare probabilities obtained by scoring answer tokens with predictions generated after a written rationale. Across 13 model-domain cells covering four retail tasks in three markets, including two using fully public data and checkpoints, the scored readout ranks outcomes more accurately in 12 of 13 cells (two-sided sign test, p approximately 0.003), by 1.5 to 14.5 points in area under the receiver operating characteristic curve (AUC). Paired bootstrap confidence intervals exclude zero...
| Authors | Touchapon Kraisingkorn, Krittin Pachtrachai, Wachiravit Modecrua |
|---|---|
| Primary category | cs.AI |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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