Calibration as a First-Class Criterion in LLM Evaluation
Authors argue LLM benchmarks should report calibration alongside accuracy for trustworthy evaluation.
The paper argues that NLP research often releases models, datasets, and benchmarks without checking whether confidence aligns with correctness. Miscalibration can harm deployment decisions and methods such as LLM-as-a-judge, synthetic data generation, and active learning. Because standard calibration metrics need only a confidence score and a correctness label, the authors say most current benchmarks can report calibration immediately, while open-ended generation remains unresolved.
- Argues every NLP metric should be paired with calibration.
- Overconfidence affects deployment and research methods.
- Most benchmarks already include confidence and correctness.
- Open-ended generation still lacks standard calibration inputs.
From the original article
1 min read at huggingface.co
Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within NLP. Methods to measure it already exist. The problem is adoption: outside this subfield, NLP research regularly introduces new models, datasets, and benchmarks without checking whether the model's confidence scores are meaningful.
By Mario Sanz-Guerrero, Katharina von der Wense · huggingface.co
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