Abstract
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-written 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.- Anthology ID:
- 2024.naacl-long.110
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1975–1997
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.110
- DOI:
- Cite (ACL):
- Jiarui Liu, Wenkai Li, Zhijing Jin, and Mona Diab. 2024. Automatic Generation of Model and Data Cards: A Step Towards Responsible AI. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1975–1997, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (Liu et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2024.naacl-long.110.pdf