[2603.20161] Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models

Computer Science > Computation and Language arXiv:2603.20161 (cs) [Submitted on 20 Mar 2026] Title:Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models Authors:Qi Cao, Andrew Gambardella, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa View a PDF of the paper titled Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models, by Qi Cao and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have demonstrated 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, introducing substantial computational overhead. To address these limitations, we propose Semantic Token Clustering (STC), an efficient uncertainty quantification method that leverages the semantic information inherently encoded in LLMs. Specifically, we group tokens into semantically consistent clusters using embedding clustering and prefix matching, and quantify uncertainty based on the probability mass aggregated over the corresponding semantic cluster. Our approach requires only a single generation and does not depend on auxiliary models. Experimental results show that STC achieves performance comparable to state-of-the-art baseli...