Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
This position paper argues that current uncertainty quantification (UQ) methods for large language models (LLMs) suffer from a category error: they are essentially unsupervised clustering algorithms that measure internal consistency rather than factual correctness. The authors identify three critical pathologies: hyperparameter sensitivity, an internal evaluation cycle that conflates stability with truth, and a lack of ground truth leading to reliance on unstable proxies. They call for a paradigm shift toward anchoring UQ in objective truth and propose a research roadmap.
[2605.19220] Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
[Submitted on 19 May 2026]
Title:Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
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Abstract:Uncertainty Quantification (UQ) is widely regarded as the primary safeguard for deploying Large Language Models (LLMs) in high-stakes domains. However, we argue that the field suffers from a category error: mainstream UQ methods for LLMs are just unsupervised clustering algorithms. We demonstrate that most current approaches inherently quantify the internal consistency of the model's generations rather than their external correctness. Consequently, current methods are fundamentally blind to factual reality and fail to detect ``confident hallucinations,'' where models exhibit high confidence in stable but incorrect answers. Therefore, the current UQ methods may create a deceptive sense of safety when deploying the models with uncertainty. In detail, we identify three critical pathologies resulting from this dependence on internal state: a hyperparameter sensitivity crisis that renders deployment unsafe, an internal evaluation cycle that conflates stability with truth, and a fundamental lack of ground truth that forces reliance on unstable proxy metrics to evaluate uncertainty. To resolve this impasse, we advocate for a paradigm shift to UQ and outline a roadmap for the research community to adopt better evaluation metrics and settings, implement mechanism changes for native uncertainty, and anchor verification in objective truth, ensuring that model confidence serves as a reliable proxy for reality.
Comments: Accepted by ICML 2026 Position Paper Track
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
MSC classes: 68T50, 68T37, 68Q32
ACM classes: I.2.7; I.2.6; I.2.4
Cite as: arXiv:2605.19220 [cs.CL]
(or arXiv:2605.19220v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.19220
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hua Wei [view email] [v1] Tue, 19 May 2026 00:47:02 UTC (607 KB)
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