Title: Optimizing AutoEval: Leveraging Synthetic Data for Model Assessment

Abstract: Relying on human-annotated validation datasets for machine learning model evaluation often entails significant costs and time investments. To mitigate these demands, researchers can utilize AI-generated synthetic data to reduce the volume of human annotations needed—a methodology known as autoevaluation. This paper introduces efficient, statistically rigorous algorithms designed to enhance sample efficiency without introducing bias. In trials involving GPT-4, these approaches demonstrated the ability to boost the effective size of human-labeled samples by as much as 50%.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC