Provable Limits and Certified Deferral for Verbalized Uncertainty in Small Language Models
Small open-weight language models increasingly run in private, offline, and cost-sensitive settings, where the key deployment question is not only what a model answers but when it should defer to a human. We study whether verbalized confidence can support risk-controlled deferral, evaluating eleven instruction-tuned models from three families, 0.5B to 14B parameters, on ARC-Challenge and TruthfulQA with 25,168 local predictions. Three theoretical results delimit what calibration can provide: strictly monotone calibration preserves the risk-coverage frontier and error-detection AUROC; temperature scaling cannot calibrate models whose confidence stays above one half while accuracy falls bel...
| Authors | Jianru Shen |
|---|---|
| Primary category | cs.CL |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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