Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models

ArXi:2603.20161v1 Announce Type: cross Large language models (LLMs) have nstrated remarkable capabilities across diverse tasks. However, the truthfulness of their outputs is not guaranteed, and their tendency toward overconfidence further limits reliability. Uncertainty quantification offers a promising way to identify potentially unreliable outputs, but most existing methods rely on repeated sampling or auxiliary models,