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README.md

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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.
+- `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 exactly 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.",
+}
+```