  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.
