While the capabilities of generative models heavily improved in different
domains (images, text, graphs, molecules, etc.), their evaluation metrics
largely remain based on simplified quantities or manual inspection with limited
practicality. To this end, we propose a framework for Multi-level Performance
Evaluation of Generative mOdels (MPEGO), which could be employed across
different domains. MPEGO aims to quantify generation performance
hierarchically, starting from a sub-feature-based low-level evaluation to a
global features-based high-level evaluation. MPEGO offers great customizability
as the employed features are entirely user-driven and can thus be highly
domain/problem-specific while being arbitrarily complex (e.g., outcomes of
experimental procedures). We validate MPEGO using multiple generative models
across several datasets from the material discovery domain. An ablation study
is conducted to study the plausibility of intermediate steps in MPEGO. Results
demonstrate that MPEGO provides a flexible, user-driven, and multi-level
evaluation framework, with practical insights on the generation quality. The
framework, source code, and experiments will be available at
this https URL.

提出了一个适用于不同领域的多层次性能评估框架 (MPEGO)，该框架从基于子特征的低层次评估到基于全局特征的高层次评估，可定制化以支持用户自定义特征，验证结果表明该框架能帮助实现对生成模型性能的灵活、可定制化和多层次的评价，适用于材料发现等多个领域。