Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three stanResearch goal: How do novel structural fidelity metrics for tabular generative models correlate with downstream performance when used to pre-train large language models on mixed-type data?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.