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.

使用大型语言模型（LLMs）提出了一种自动化生成方法，以处理当前人类生成的模型和数据卡中的信息不完整问题，并建立了 CardBench 数据集和 CardGen 流水线，实现了生成模型和数据卡的完整性、客观性和真实性的提升，是负责任的 AI 文档实践中迈出的重要一步，确保更好的可追溯性和问责制。