Evaluating the Diversity of AI-Generated Content with Diversity Profiles
Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous. Existing approaches typically represent generated samples in an embedding space, compute pairwise distances or similarities, and aggregate them into a single scalar score. Such scalar summaries are convenient, but they often encode different inductive biases and may yield contradictory rankings of the same sample sets. In this paper, we argue that diversity evaluation for AI-generated content is intrinsically under-specified when reduced to a single number. We first review representative diversity metrics, and then diagnose their limitation...
| Authors | Xiuyuan Hu, Xuege Hou, Guoqing Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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