Rethinking Uncertainty Evaluation in Large Language Models

Jul 23, 2026

A new arXiv preprint argues that calibration, the standard method for evaluating confidence in large language models (LLMs), is insufficient because it allows for incoherent and unfaithful probability estimates. The authors introduce a new framework with three axes—structural coherence, faithfulness, and usefulness—to more rigorously assess LLM uncertainty. They find that commonly used confidence estimators can appear well-calibrated while still violating these coherence criteria, indicating that current LLM confidence scores may not represent true probabilistic beliefs.

Why it matters: This challenges the reliability of LLM confidence estimates, raising concerns about their trustworthiness in applications where accurate uncertainty quantification is critical.

Full story at: arXiv AI/ML ↗