  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.
