TECP: Token-Entropy Conformal Prediction for LLMs
In this paper, we introduce Token-Entropy Conformal Prediction, a novel framework that leverages token-level entropy as a logit-free, reference-free uncertainty measure and integrates it into a split conformal prediction (CP) pipeline to construct prediction sets with formal coverage guarantees.