LCATS: LLM-Guided Constraint-Aware Tabular Data Synthesis

Abstract

Tabular data synthesis must satisfy two complementary quality criteria. At the global level, synthetic records must faithfully reproduce the statistical characteristics of the original dataset, including marginal distributions and inter-variable correlations. At the local level, each individual record must satisfy domain-specific logical constraints such as algebraic identities, conditional dependencies, and mutual exclusivity rules. Existing diffusion-based methods excel at global statistical fidelity but frequently violate hard logical constraints, while LLM-based methods offer stronger constraint adherence but suffer from autoregressive distributional drift that degrades global fidelity. In this paper, we propose LLM-Guided Constraint-Aware Tabular Data Synthesis (LCATS), an end-to-end framework that decouples global distribution modeling from local constraint enforcement. LCATS comprises four modules. The Analysis module uses an LLM to discover candidate constraints, encodes them as executable verification scripts, and validates them against the original data to eliminate hallucinated patterns. The Synthesis module employs a diffusion model to generate statistically faithful synthetic data. The Optimization module repairs constraint-violating records through a constrained weighted optimization problem, which dynamically assigns column-level adjustment penalties to minimize distributional distortion. The Evolution module further mitigates distributional distortion through iterative feedback during constraint-aware optimization. Extensive experiments on seven benchmark datasets demonstrate that LCATS achieves strong overall performance against diffusion-based, LLM-based, and hybrid baselines in ?-precision, ?-recall, and downstream predictive performance, while producing zero constraint violations. The code is available at https://github.com/SJTU-DMTai/LCATS.