MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation
The rapid proliferation of Generative AI necessitates rigorous documentation standards for transparency and governance. However, manual creation of Model and Data Cards is not scalable, while automated approaches lack large-scale, high-fidelity benchmarks for systematic evaluation. We introduce MetaGAI, a comprehensive benchmark comprising 2,541 verified document triplets constructed through semantic triangulation of academic papers, GitHub repositories, and Hugging Face artifacts. Unlike prior single-source datasets, MetaGAI employs a multi-agent framework with specialized Retriever, Generator, and Editor agents, validated through four-dimensional human-in-the-loop assessment, including ...
| Authors | Haoxuan Zhang, Ruochi Li, Yang Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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