Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912410748583936 |
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| author | Winata, Genta Indra Anugraha, David Liu, Emmy Aji, Alham Fikri Hung, Shou-Yi Parashar, Aditya Irawan, Patrick Amadeus Zhang, Ruochen Yong, Zheng-Xin Cruz, Jan Christian Blaise Muennighoff, Niklas Kim, Seungone Zhao, Hanyang Kar, Sudipta Suryoraharjo, Kezia Erina Adilazuarda, M. Farid Lee, En-Shiun Annie Purwarianti, Ayu Wijaya, Derry Tanti Choudhury, Monojit |
| author_facet | Winata, Genta Indra Anugraha, David Liu, Emmy Aji, Alham Fikri Hung, Shou-Yi Parashar, Aditya Irawan, Patrick Amadeus Zhang, Ruochen Yong, Zheng-Xin Cruz, Jan Christian Blaise Muennighoff, Niklas Kim, Seungone Zhao, Hanyang Kar, Sudipta Suryoraharjo, Kezia Erina Adilazuarda, M. Farid Lee, En-Shiun Annie Purwarianti, Ayu Wijaya, Derry Tanti Choudhury, Monojit |
| contents | High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency, they are largely descriptive and do not provide standardized, measurable methods for evaluating data quality. Similarly, metadata requirements at conferences promote accountability but are inconsistently enforced. To address these limitations, this position paper advocates for the integration of systematic, rubric-based evaluation metrics into the dataset review process-particularly as submission volumes continue to grow. We also explore scalable, cost-effective methods for synthetic data generation, including dedicated tools and LLM-as-a-judge approaches, to support more efficient evaluation. As a call to action, we introduce DataRubrics, a structured framework for assessing the quality of both human- and model-generated datasets. Leveraging recent advances in LLM-based evaluation, DataRubrics offers a reproducible, scalable, and actionable solution for dataset quality assessment, enabling both authors and reviewers to uphold higher standards in data-centric research. We also release code to support reproducibility of LLM-based evaluations at https://github.com/datarubrics/datarubrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability Winata, Genta Indra Anugraha, David Liu, Emmy Aji, Alham Fikri Hung, Shou-Yi Parashar, Aditya Irawan, Patrick Amadeus Zhang, Ruochen Yong, Zheng-Xin Cruz, Jan Christian Blaise Muennighoff, Niklas Kim, Seungone Zhao, Hanyang Kar, Sudipta Suryoraharjo, Kezia Erina Adilazuarda, M. Farid Lee, En-Shiun Annie Purwarianti, Ayu Wijaya, Derry Tanti Choudhury, Monojit Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Audio and Speech Processing High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency, they are largely descriptive and do not provide standardized, measurable methods for evaluating data quality. Similarly, metadata requirements at conferences promote accountability but are inconsistently enforced. To address these limitations, this position paper advocates for the integration of systematic, rubric-based evaluation metrics into the dataset review process-particularly as submission volumes continue to grow. We also explore scalable, cost-effective methods for synthetic data generation, including dedicated tools and LLM-as-a-judge approaches, to support more efficient evaluation. As a call to action, we introduce DataRubrics, a structured framework for assessing the quality of both human- and model-generated datasets. Leveraging recent advances in LLM-based evaluation, DataRubrics offers a reproducible, scalable, and actionable solution for dataset quality assessment, enabling both authors and reviewers to uphold higher standards in data-centric research. We also release code to support reproducibility of LLM-based evaluations at https://github.com/datarubrics/datarubrics. |
| title | Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.01789 |