Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
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 ...
| Authors | Tiejin Chen, Longchao Da, Xiaoou Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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