Automatic Model Card Generation Using an LLM

2026-08-25

AI Summary

本文提出MCTidy和MCGenie两种基于大语言模型的方法，以标准化模板重新组织现有模型卡或直接从模型库数据生成新的模型卡，提高机器学习模型的透明度、可比性和解释性。

Abstract

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.

Problem

• model cards
• machine learning models
• transparency
• usability
• accountability

Innovation

• MCTidy
• MCGenie
• LLM-based approach
• standardized template
• model card generation