{
  "id": 5352,
  "url": "https://arxiv.org/abs/2604.23539v1",
  "title": "MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation",
  "summary": "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 ",
  "authors": "Haoxuan Zhang, Ruochi Li, Yang Zhang, Zhenni Liang, Junhua Ding, Ting Xiao et al.",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": "huggingface",
  "regions": null,
  "published_at": "2026-04-26T05:24:03.000Z",
  "fetched_at": "2026-07-14T16:31:40.222Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5352",
  "original_url": "https://arxiv.org/abs/2604.23539v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}