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arXiv Machine Learning By Anna-Christina Glock, Thomas H\"utter, Johannes F\"urnkranz, Wolfram W\"o{\ss}, Christine Dominka-Kiss, Lisa Ehrlinger

Data Quality Rule Generation with LLMs

Read the original on arXiv Machine Learning →

The paper introduces LeDQeR, an approach that uses large language models to automatically generate data quality rules for rule‑based enterprise tools. It follows a generate‑filter framework where the LLM proposes candidate rules from dirty data, and four filters ensure the rules are executable, correct, generalizable, and non‑redundant. Experiments show that LeDQeR produces effective, compact rule sets across diverse datasets and error types.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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