AutoEval Framework

Automated Model Evaluation (AutoEval) frameworks aim to assess machine learning model performance without relying on labeled test datasets, addressing a critical limitation in real-world applications where such data is often scarce or unavailable. Current research focuses on developing more efficient and accurate AutoEval methods, exploring techniques like information-theoretic approaches, contrastive learning, and energy-based models to estimate performance from unlabeled data. These advancements are significant because they promise to streamline model evaluation, reduce reliance on labeled data, and accelerate the development and deployment of machine learning systems across various domains.