  Tabular data is among the oldest and most ubiquitous forms of data. However,
the generation of synthetic samples with the original data's characteristics
remains a significant challenge for tabular data. While many generative models
from the computer vision domain, such as variational autoencoders or generative
adversarial networks, have been adapted for tabular data generation, less
research has been directed towards recent transformer-based large language
models (LLMs), which are also generative in nature. To this end, we propose
GReaT (Generation of Realistic Tabular data), which exploits an auto-regressive
generative LLM to sample synthetic and yet highly realistic tabular data.
Furthermore, GReaT can model tabular data distributions by conditioning on any
subset of features; the remaining features are sampled without additional
overhead. We demonstrate the effectiveness of the proposed approach in a series
of experiments that quantify the validity and quality of the produced data
samples from multiple angles. We find that GReaT maintains state-of-the-art
performance across numerous real-world and synthetic data sets with
heterogeneous feature types coming in various sizes.
