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arXiv · 2403.07008

AutoEval Done Right: Using Synthetic Data for Model Evaluation

Pierre Boyeau·Anastasios N. Angelopoulos·Nir Yosef·Jitendra Malik·Michael I. Jordan
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Abstract

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. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.

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Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan. 2024-03-09. AutoEval Done Right: Using Synthetic Data for Model Evaluation. https://arxiv.org/abs/2403.07008

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