LLM Calibration Hides Localized Overconfidence Patterns
A recent study published on arXiv indicates that conventional calibration metrics for large language models (LLMs) may obscure critical localized miscalibration issues. The authors introduce a diagnostic framework that develops a calibration-aware representation of the input space and assesses signed local miscalibration using kernel smoothing techniques. Evaluating four real-world benchmarks and twelve different LLMs, they discover widespread input-dependent calibration variability, revealing that models tend to be overly confident in certain cases while lacking confidence in others. This research underscores the necessity for more detailed reliability assessments that go beyond just global confidence metrics.
Key facts
- Study published on arXiv with ID 2605.13484v1
- Proposes framework for discovering hidden miscalibration regimes without predefined data slices
- Defines miscalibration field and estimates it via kernel smoothing in learned geometry
- Tested on four real-world LLM benchmarks and twelve LLMs
- Finds input-dependent calibration heterogeneity is prevalent
- Models may be systematically overconfident on some inputs and underconfident on others
- Global reliability diagnostics can obscure localized calibration failures
- Calibration is typically evaluated by comparing model confidence with empirical correctness
Entities
Institutions
- arXiv