Holistic Evaluation

Holistic evaluation aims to comprehensively assess the performance of complex models, moving beyond single metrics to capture diverse aspects like robustness, fairness, and generalizability across various tasks and domains. Current research focuses on developing standardized benchmarks and evaluation frameworks for diverse model types, including large language models, protein foundation models, and video and medical imaging AI, often incorporating multiple metrics and analyzing factors influencing performance. This multifaceted approach is crucial for improving model transparency, identifying limitations, and ultimately driving the development of more reliable and trustworthy AI systems across numerous scientific and practical applications.