The provided text outlines the historical shift in generative artificial intelligence evaluation, moving from basic statistical word-overlap metrics to sophisticated, multidimensional assessment frameworks. Modern strategies now emphasize human alignment, expert-level multimodal reasoning, and the use of highly capable models to act as autonomous judges for other AI systems. As technology advances toward agentic capabilities, evaluation must prioritize task completion and tool accuracy over simple text generation. Furthermore, the industry is increasingly focused on safety red-teaming and mitigating the risks of data contamination to ensure benchmark integrity. This evolution highlights a growing divergence between theoretical academic research and the practical reliability requirements of industrial deployments.
