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.
@article{arxiv.2403.07008,
title = {AutoEval Done Right: Using Synthetic Data for Model Evaluation},
author = {Pierre Boyeau and Anastasios N. Angelopoulos and Nir Yosef and Jitendra Malik and Michael I. Jordan},
journal= {arXiv preprint arXiv:2403.07008},
year = {2024}
}