mlsanityData to 7 Oct 2026

Automatic Generation of Model and Data Cards: A Step Towards Responsible AI

arXiv 2405.06258 ↗First posted 10 May 2024cs.CLcited by 9

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Abstract (arXiv)

In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 28 of its 47 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

28 of 47 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 3.

9 papers.

  1. 2025Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Modelscited by 8
  2. 2025Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentationcited by 4
  3. 2025EvalCards: A Framework for Standardized Evaluation Reportingcited by 4
  4. 2024Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)cited by 1
  5. 2025VERBA: Verbalizing Model Differences Using Large Language Modelscited by 0
  6. 2025AI Transparency Atlas: Framework, Scoring, and Real-Time Model Card Evaluation Pipelinecited by 0
  7. 2026Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Modelscited by 0
  8. 2026AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generationcited by 0
  9. 2026MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generationcited by 0