Automated Capability Evaluation of Foundation Models

Current evaluation frameworks for foundation models rely heavily on fixed, manually curated benchmarks, limiting their ability to capture the full breadth of model capabilities. This paper introduces Active learning for Capability Evaluation (ACE), a novel framework for scalable, automated, and fine-grained evaluation of foundation models. ACE leverages the knowledge embedded in powerful language models to decompose a domain into semantically meaningful capabilities and generate diverse evaluation tasks, significantly reducing human effort. To maximize coverage and efficiency, ACE models a subject model's performance as a capability function over a latent semantic space and uses active learning to prioritize the evaluation of the most informative capabilities. This adaptive evaluation strategy enables cost-effective discovery of strengths, weaknesses, and failure modes that static benchmarks may miss. Our results suggest that ACE provides a more complete and informative picture of model capabilities, which is essential for safe and well-informed deployment of foundation models.