Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables
Summary: Auto-Test learns Semantic-Domain Constraints from corpora for unsupervised error detection, removing per-table expert specification. Optimization-based distillation yields a provably reliable core that detects errors and augments cleaning; 2400-column benchmark and code released. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Qixu Chen (Hong Kong University of Science and Technology)
- 2. Yeye He (Microsoft)
- 3. Raymond Chi-Wing Wong (Hong Kong University of Science and Technology)
- 4. Weiwei Cui (Microsoft)
- 5. Song Ge (Microsoft)
- 6. Haidong Zhang (Microsoft)
- 7. Dongmei Zhang (Microsoft)
- 8. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@inproceedings{chen_sigmod25,
title = {{Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables}},
author = {Chen, Qixu and He, Yeye and Wong, Raymond Chi-Wing and Cui, Weiwei and Ge, Song and Zhang, Haidong and Zhang, Dongmei and Chaudhuri, Surajit},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725396},
url = {https://dl.acm.org/doi/10.1145/3725396},
year = {2025}
}
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