ZeroED is a hybrid framework that combines LLM reasoning capabilities with traditional methods to detect errors in tabular data without requiring labeled training examples.

• Overcomes limitations of traditional error detection methods that rely heavily on manual criteria
• Leverages LLMs' contextual understanding while compensating for their limitations
• Reduces human effort required for data validation and cleaning
• Enhances data security by identifying corrupted or incorrect data that could create vulnerabilities

This approach provides organizations with a more efficient way to ensure data integrity, a critical foundation for secure data operations and trustworthy analytics.

Original Paper: ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning