# Automatic Model Card Generation Using an LLM

arXiv: https://arxiv.org/abs/2608.24807 · HTML: https://mlsanity.com/paper/2608.24807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models](https://mlsanity.com/paper/2404.18796/) (2024)
- [On scalable oversight with weak LLMs judging strong LLMs](https://mlsanity.com/paper/2407.04622/) (2024)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Efficient Prompting Methods for Large Language Models: A Survey](https://mlsanity.com/paper/2404.01077/) (2024)
- [A Methodology for Creating AI FactSheets](https://mlsanity.com/paper/2006.13796/) (2020)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [Understanding the Effects of Iterative Prompting on Truthfulness](https://mlsanity.com/paper/2402.06625/) (2024)
- [Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends](https://mlsanity.com/paper/2401.13177/) (2024)
- [What's documented in AI? Systematic Analysis of 32K AI Model Cards](https://mlsanity.com/paper/2402.05160/) (2024)
- [Towards evaluating and eliciting high-quality documentation for intelligent systems](https://mlsanity.com/paper/2011.08774/) (2020)
- [Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models](https://mlsanity.com/paper/2410.09012/) (2024)
- [The State of Documentation Practices of Third-party Machine Learning Models and Datasets](https://mlsanity.com/paper/2312.15058/) (2023)
- [Documenting Ethical Considerations in Open Source AI Models](https://mlsanity.com/paper/2406.18071/) (2024)

## Abstract (arXiv)

Model cards are structured documents that summarize key information about machine learning models to improve transparency, usability, and accountability. However, they often lack a consistent structure, and many models provide no model cards, making comparison and interpretation difficult. This paper presents two contributions. First, we propose MCTidy, an LLM-based approach that reorganizes existing model cards into a standardized template to improve clarity and comparability. Second, we introduce MCGenie, an LLM-based system that generates model cards directly from model repository data. We apply MCTidy to 48 Hugging Face model cards and evaluate information retention, section alignment, hallucination, and stability. Our findings show high information retention with minimal textual loss, accurate section assignment, rare hallucinations primarily in descriptive sections, and strong stability across runs. We assess MCGenie by generating model cards for the same 48 models and assessing semantic similarity, factual correctness, and sensitivity to input resources. The generated model cards achieved high semantic similarity (mean around 0.9); over half were fully correct, and most remaining errors were minor. Generation quality depended strongly on the availability of supporting resources, particularly associated papers. Overall, our findings demonstrate the potential of LLM-based methods to enable scalable, standardized model card documentation.