This research evaluates how well large language models can generate high-quality synthetic tabular data while addressing privacy concerns.

• Introduces frameworks to assess synthetic data quality beyond the standard train-synthetic-test paradigm
• Compares LLM performance against traditional generative models for structured data generation
• Identifies strengths and limitations of using language models for tabular data synthesis
• Provides insights on maintaining data utility while preserving privacy

Why it matters for Security: As organizations seek alternatives to sharing sensitive data, this research establishes evaluation methods to ensure synthetic data maintains utility while addressing privacy requirements. This enables secure data sharing across business units and with external partners.

Assessing Generative Models for Structured Data