# Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables

arXiv: https://arxiv.org/abs/2504.10762 · HTML: https://mlsanity.com/paper/2504.10762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 83 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Table-GPT: Table-tuned GPT for Diverse Table Tasks](https://mlsanity.com/paper/2310.09263/) (2023)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)
- [Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning](https://mlsanity.com/paper/2410.12164/) (2024)
- [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python](https://mlsanity.com/paper/2104.00841/) (2021)
- [Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes](https://mlsanity.com/paper/2104.04659/) (2021)
- [Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph](https://mlsanity.com/paper/2306.12515/) (2023)
- [Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines](https://mlsanity.com/paper/2306.02421/) (2023)
- [Automatic Data Repair: Are We Ready to Deploy?](https://mlsanity.com/paper/2310.00711/) (2023)
- [Auto-Tag: Tagging-Data-By-Example in Data Lakes](https://mlsanity.com/paper/2112.06049/) (2021)
- [AdaTyper: Adaptive Semantic Column Type Detection](https://mlsanity.com/paper/2311.13806/) (2023)
- [Extracting Syntactic Patterns from Databases](https://mlsanity.com/paper/1710.11528/) (2017)
- [A Hybrid Data Cleaning Framework using Markov Logic Networks](https://mlsanity.com/paper/1903.05826/) (2019)

## Built on by (most cited first, 2 of 2)

- [MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.05587/) (2025)
- [Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models](https://mlsanity.com/paper/2607.19847/) (2026)

## Abstract (arXiv)

Data cleaning is a long-standing challenge in data management. While powerful logic and statistical algorithms have been developed to detect and repair data errors in tables, existing algorithms predominantly rely on domain-experts to first manually specify data-quality constraints specific to a given table, before data cleaning algorithms can be applied. In this work, we propose a new class of data-quality constraints that we call Semantic-Domain Constraints, which can be reliably inferred and automatically applied to any tables, without requiring domain-experts to manually specify on a per-table basis. We develop a principled framework to systematically learn such constraints from table corpora using large-scale statistical tests, which can further be distilled into a core set of constraints using our optimization framework, with provable quality guarantees. Extensive evaluations show that this new class of constraints can be used to both (1) directly detect errors on real tables in the wild, and (2) augment existing expert-driven data-cleaning techniques as a new class of complementary constraints. Our extensively labeled benchmark dataset with 2400 real data columns, as well as our code are available at https://github.com/qixuchen/AutoTest to facilitate future research.