Quantifies LLM uncertainty in a single generation pass without auxiliary models or repeated sampling.
March 23, 2026
Original Paper
Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
arXiv · 2603.20161
The Takeaway
Current uncertainty quantification methods typically require 10-20x the compute cost via multiple sampling. By clustering semantic tokens in the embedding space, this method enables reliable halluncination detection at 1x inference cost.
From the 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 Clust