Christopher P. Monterola

When Models Hesitate: Answer Instability as a Label-Free Uncertainty Signal for LLMs

This work proposes answer instability, defined as the variability of a model’s final answer across repeated stochastic generations of the same prompt, as a simple, label-free, and black-box uncertainty signal, and demonstrates its utility for selective prediction and targeted repair, improving reliability without access to internal probabilities or additional training.