# AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation Source: [AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation]() Collection index: [llms.txt]() Data updated: 2026-04-15T05:00:04.591Z Authors: Haoxuan Zhang, Ruochi Li, Zhenni Liang, Mehri Sattari, Phat Vo, Collin Qu, Ting Xiao, Junhua Ding, Yang Zhang, Haihua Chen Published: 2026-03-16T04:02:56.000Z ## Abstract Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for generating model and data cards still face three major challenges: (i) static templates, as most systems rely on fixed query templates that cannot adapt to diverse paper structures or evolving documentation requirements; (ii) information scarcity, since web-scale repositories such as Hugging Face often contain incomplete or inconsistent metadata, leading to missing or noisy information; and (iii) lack of benchmarks, as the absence of standardized datasets and evaluation protocols hinders fair and reproducible assessment of documentation quality. To address these limitations, we propose AdaQE-CG, an Adaptive Query Expansion for Card Generation framework that combines dynamic information extraction with cross-card knowledge transfer. Its Intra-Paper Extraction via Context-Aware Query Expansion (IPE-QE) module iteratively refines extraction queries to recover richer and more complete information from scientific papers and repositories, while its Inter-Card Completion using the MetaGAI Pool (ICC-MP) module fills missing fields by transferring semantically relevant content from similar cards in a curated dataset. In addition, we introduce MetaGAI-Bench, the first large-scale, expert-annotated benchmark for evaluating GAI documentation. Comprehensive experiments across five quality dimensions show that AdaQE-CG substantially outperforms existing approaches, exceeds human-authored data cards, and approaches human-level quality for model cards. Code, prompts, and data are publicly available at: https://github.com/haoxuan-unt2024/AdaQE-CG. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Data Card Generation on MetaGAI-Bench Data Cards](): Data Card Generation; MetaGAI-Bench Data Cards; Faithfulness Rank: 1. - [Model Card Generation on MetaGAI-Bench Model Cards](): Model Card Generation; MetaGAI-Bench Model Cards; Faithfulness (G1) Rank: 2. ## Original sources and implementation [Original paper]() [PDF]()