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ICML2025顶会

AutoEval Done Right: Using Synthetic Data for Model Evaluation

Pierre Boyeau, Anastasios Nikolas Angelopoulos, Tianle Li, Nir Yosef, Jitendra Malik, Michael I. Jordan

出版方
2025年份
24顶会引用

摘要

The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased.

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