ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation 事件

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation arXiv:2604.23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks. We propose ProEval, a proactive evaluation framework that leverages transfer learning to efficiently estimate performance and identify failure cases. ProEval employs pre-tr