Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models

AbstractLarge Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, their limited truthfulness and tendency toward overconfidence constrain their reliability in factual tasks. Uncertainty quantification offers a promising approach to identifying potentially unreliable outputs from LLMs. Yet most existing methods rely on repeated sampling or auxiliary models, which substantially increase computational overhead. To address these limitations, we propose an efficient uncertainty quantification method that leverages semantic information inherently encoded in LLMs. Specifically, we group tokens into semantically consistent clusters based on embedding clustering and prefix matching, and compute a cluster-based score at each decoding step to represent uncertainty. Our approach requires only a single generation and does not depend on any auxiliary models. Experiments on multiple datasets and models demonstrate that our method achieves performance comparable to existing baselines while substantially reducing computational overhead.