Multi Level Evaluation

Multi-level evaluation aims to move beyond single-metric assessments of complex systems, such as large language models or image generation algorithms, by providing a hierarchical analysis of performance across different levels of granularity. Current research focuses on developing frameworks that decompose evaluations into sub-components (e.g., object vs. background in images, or subfields within a knowledge domain for LLMs), enabling more nuanced understanding of strengths and weaknesses. This approach enhances transparency, facilitates targeted model improvements, and offers more reliable insights into the capabilities and limitations of these increasingly sophisticated systems, ultimately leading to more robust and responsible AI development.