Abstract: Tabular data holds significant value due to its widespread application in critical domains such as finance and healthcare.However,the effective utilization of tabular data is often constrained by data scarcity,class imbalance,and stringent privacy regulations.To address these challenges,synthesizing samples that are statistically highly similar to real data through generative models has emerged as a novel solution,aiming to enhance data availability and protect user privacy.The technological development path in this field has progressively evolved from traditional deep learning models to cutting-edge paradigms.Early explorations are represented by Variational Autoencoders and Generative Adversarial Networks,but these methods often face bottlenecks such as training instability and mode collapse,affecting the quality of generated data.To overcome these difficulties,diffusion models have emerged,demonstrating significant advantages in generating high-fidelity and diverse samples through a progressive denoising process.Nevertheless,the core of these models remains the imitation of statistical distributions,lacking an understanding of real-world common sense.Consequently,the latest research has shifted towards methods based on Large Language Models(LLMs),leveraging their rich world knowledge to generate synthetic tabular data that is not only statistically authentic but also logically and semantically more reasonable.A systematic review of this field aims to provide researchers and practitioners with a comprehensive understanding of the technology and offer decision-making references for selecting the most appropriate technical path in different application scenarios.

Key words: Tabular data generation,Large language model,Generative methods