CAST: Conditional Generation and Adversarial Selection for Tabular Data Synthesis with Long Text Columns

Synthesizing tabular data with long text columns requires preserving structured attributes, text semantics, and the predictive relationships between them. Existing methods improve individual records through generator fine-tuning or post-generation filtering, but high record-level quality *does not ensure that the resulting table preserves text–attribute relationships at the dataset level*. We propose **CAST**, a conditional generation and adversarial selection framework that keeps the text generator frozen and combines candidate construction under structural constraints with dataset-level selection. Structure-Aware Semantic Generation (SSG) builds multiple text candidates from a shared semantic plan and distinct narrative roles. Conditional Adversarial Selection (CAS) reselects candidates by matching task features to those of real references, with feedback from the current synthetic table and a fixed domain discriminator. Reliability-Guided Conservative Selection (RCS) gates and reweights task feedback using validation performance, then mixes selected candidates with preset-role candidates to reduce reliance on proxy scores. Across three tabular datasets with long text columns, CAST achieves higher downstream utility than the compared synthesis methods, with gains across different generator backbones and modeling strategies.