Calibration as a First-Class Criterion in LLM Evaluation

2026-09-22

AI Summary

论文针对语言模型校准问题，提出在NLP研究中将校准作为评价模型的重要标准，通过已有数据和方法提升模型可信度。

Abstract

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. We argue that this adoption gap is a major obstacle to trustworthy LLM evaluation. Miscalibration causes problems in two distinct areas: at deployment, where overconfident mistakes cause real harm, and inside the research pipeline, where methods like LLM-as-a-judge, synthetic data generation, and active learning rely on calibrated confidence without verifying it. Standard calibration metrics only require two inputs per example: a confidence score and a correctness judgment. Most benchmarks in use today already provide both, meaning calibration can be reported immediately. For open-ended generation, however, defining these two inputs is still an open challenge. We argue that each NLP subfield should pair its main performance metric with a calibration score and call for treating calibration as an essential property of every model rather than a niche topic.

Problem

• Calibration
• NLP
• LLM Evaluation
• Adoption Gap
• Trustworthy

Innovation

• Calibration
• LLM Evaluation
• Confidence Scores
• NLP Subfields