# AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation

arXiv: https://arxiv.org/abs/2604.09617 · HTML: https://mlsanity.com/paper/2604.09617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 24 of 24)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.10704/) (2024)
- [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024)
- [Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems](https://mlsanity.com/paper/2101.09385/) (2021)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://mlsanity.com/paper/2505.23628/) (2025)
- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://mlsanity.com/paper/2404.12691/) (2024)
- [Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face](https://mlsanity.com/paper/2508.06811/) (2025)
- [Automatic Generation of Model and Data Cards: A Step Towards Responsible AI](https://mlsanity.com/paper/2405.06258/) (2024)
- [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022)
- [Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments](https://mlsanity.com/paper/2312.06153/) (2023)
- [The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track](https://mlsanity.com/paper/2410.22473/) (2024)
- [Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements](https://mlsanity.com/paper/2507.06014/) (2025)
- [Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation](https://mlsanity.com/paper/2503.13463/) (2025)
- [Blueprints of Trust: AI System Cards for End to End Transparency and Governance](https://mlsanity.com/paper/2509.20394/) (2025)

## Abstract (arXiv)

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.