Data Quality Rule Generation with LLMs
The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For example, a wrong drug unit in a patient record can lead to life-threatening medication errors, and a missing street number in an address to failed deliveries. Companies often employ rule-based enterprise data quality (DQ) tools, which allow domain experts to specify rules to validate the data over time. While rule-based DQ tools are computationally efficient and provide explainable reports, maintaining a comprehensive rule set manually is challenging, as domain experts often overlook essential rules, especially in complex domains and larg...
| Authors | Anna-Christina Glock, Thomas Hütter, Johannes Fürnkranz et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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