Paper: Calibration Should Be a First-Class Criterion in LLM Evaluation
Mario Sanz-Guerrero · hf · 2026-09-24
This paper argues calibration — the alignment between a model's expressed confidence and empirical correctness — should be treated as an essential property of every LLM, not a niche NLP topic. The problem is adoption, not measurement: new models, datasets, and benchmarks regularly ship without checking whether confidence scores are meaningful.
- Deployment harm: overconfident mistakes cause real damage.
- Research pipeline: LLM-as-a-judge, synthetic data, and active learning rely on calibrated confidence without verifying it.
- Easy to adopt: standard calibration metrics need only a confidence score and correctness judgment per example — most benchmarks already provide both; open-ended generation remains an open challenge.
The authors call for each NLP subfield to pair its main metric with a calibration score.
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