AutoEval Done Right: Using Synthetic Data for Model Evaluation
This work addresses the cost and time challenges in model evaluation for machine learning practitioners, presenting an incremental improvement over existing autoevaluation methods.
The paper tackled the problem of expensive and time-consuming model evaluation by proposing efficient and statistically principled algorithms for autoevaluation using synthetic data, resulting in up to a 50% increase in effective human-labeled sample size in experiments with GPT-4.
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.