LLM-TabFlow introduces a novel approach for generating high-quality synthetic tabular data while maintaining logical relationships between columns.

• Privacy protection while generating realistic data for sensitive sectors
• Logical relationship preservation between columns—a key innovation over existing methods
• Industry applications across healthcare, finance, and supply chains
• Data scarcity mitigation through synthetic data generation

Healthcare Impact: This research enables medical institutions to share realistic patient data without privacy concerns, allowing for broader research collaboration, improved model training, and better clinical decision support while maintaining regulatory compliance.

LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation