CATS : An enhanced framework in LLM-based tabular data synthesis by correlation augmentation

As AI technology advances, sectors such as finance and healthcare are increasingly adopting AI tools. However, due to privacy concerns that arise with the use of AI and the high cost of real data collection, generating realistic tabular data to replace original data has become an increasingly popular solution to address these limitations. Although the output of existing tabular data generation algorithms seems to match the distribution of original data, they often fail to preserve the correlations present in the original data. This oversight can lead to significant negative impacts on downstream tasks. This paper proposes a tabular data synthesis enhancement framework based on Large Language Model (LLM), i.e., C orrelation A ugmentation T abular data S ynthesis ( CATS ), which emphasizes improving the quality of generated tabular data while preserving the correlations between the features of the original data. We explore the technical details of the CATS framework and demonstrate that CATS achieves state-of-the-art performance compared with baseline methods across seven benchmark datasets. Additionally, it enhances the performance of existing strong LLM-based tabular data generators by an average improvement of over 4.7% across all datasets.