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 Machine Learning
Sep 22

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

The study evaluates how quantization affects accuracy and safety of five 7‑8B language models on clinical benchmarks. INT8 GPTQ shows minimal degradation (≤1.9%) across tasks, while INT4 causes substantial, model‑dependent drops, especially in high‑risk scenarios and safety metrics. Recovery methods such as clinical calibration substitution and QLoRA fine‑tuning yield mixed results, underscoring the need for task‑specific validation.

By Leonard Twagirayezu, Prasenjit Mitra