Datasets:
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---
# Example metadata to be added to a dataset card.
# Full dataset card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
language:
- en
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
tags:
- RAG
- model card generation
- responsible AI
configs: # Optional. This can be used to pass additional parameters to the dataset loader, such as `data_files`, `data_dir`, and any builder-specific parameters
- config_name: model_card # Name of the dataset subset, if applicable. Example: default
data_files:
- split: test
path: model_card_test.csv
- split: whole
path: model_card_whole.csv
- config_name: data_card
data_files:
- split: whole
path: data_card_whole.csv
---
# Automatic Generation of Model and Data Cards: A Step Towards Responsible AI
The work has been accepted to NAACL 2024 Oral.
**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.
**Paper Arxiv**: https://arxiv.org/abs/2405.06258
**ACL Anthology**: https://aclanthology.org/2024.naacl-long.110/
**Repository and Code**: https://github.com/jiarui-liu/AutomatedModelCardGeneration
**Dataset descriptions**:
- `model_card_test.csv`: Contains the test set used for model card generation. We collected the model cards and data cards from the HuggingFace page as of October 1, 2023.
- `model_card_whole.csv`: Represents the complete dataset excluding the test set.
- `data_card_whole.csv`: Represents the complete dataset for data card generation.
- **Additional files**: Other included files may be useful for reproducing our work.
Disclaimer: Please forgive me for not creating this data card as described in our paper. We promise to give it some extra love and polish when we have more time! 🫠
**Citation**: If you find our work useful, please cite as follows :)
```
@inproceedings{liu-etal-2024-automatic,
title = "Automatic Generation of Model and Data Cards: A Step Towards Responsible {AI}",
author = "Liu, Jiarui and
Li, Wenkai and
Jin, Zhijing and
Diab, Mona",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.110",
doi = "10.18653/v1/2024.naacl-long.110",
pages = "1975--1997",
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.",
}
```
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