Calibrating Semantic Uncertainty from Observable Language-Model Probabilities
Researchers introduce a 'semantic map' method that translates language model word probabilities into calibrated uncertainty estimates over meaningful latent states, such as diagnoses or operational conditions. This approach uses held-out calibration to produce posterior estimates that are stable under paraphrasing and outperform raw language model probabilities. The method is validated on Federal Reserve economic texts and controlled simulations, demonstrating improved uncertainty quantification for professional decision-making contexts.
Why it matters: The work provides a statistically principled way to obtain reliable, auditable uncertainty estimates from language models, addressing a key challenge for their use in high-stakes professional applications.
Full story at: arXiv Statistical ML ↗