TabuLa: Harnessing Language Models for Tabular Data Synthesis
Zilong Zhao Robert Birke Lydia Chen
Given the ubiquitous use of tabular data in industries and the growing
concerns in data privacy and security, tabular data synthesis emerges as a
critical research area. The recent state-of-the-art methods show that large
language models (LLMs) can be adopted to generate realistic tabular data. As
LLMs pre-process tabular data as full text, they have the advantage of avoiding
the curse of dimensionality associated with one-hot encoding high-dimensional
data. However, their long training time and limited re-usability on new tasks
prevent them from replacing exiting tabular generative models. In this paper,
we propose Tabula, a tabular data synthesizer based on the language model
structure. Through Tabula, we demonstrate the inherent limitation of employing
pre-trained language models designed for natural language processing (NLP) in
the context of tabular data synthesis. Our investigation delves into the
development of a dedicated foundational model tailored specifically for tabular
data synthesis. Additionally, we propose a token sequence compression strategy
to significantly reduce training time while preserving the quality of synthetic
data. Extensive experiments on six datasets demonstrate that using a language
model structure without loading the well-trained model weights yields a better
starting model for tabular data synthesis. Moreover, the Tabula model,
previously trained on other tabular data, serves as an excellent foundation
model for new tabular data synthesis tasks. Additionally, the token sequence
compression method substantially reduces the model's training time. Results
show that Tabula averagely reduces 46.2% training time per epoch comparing to
current LLMs-based state-of-the-art algorithm and consistently achieves even
higher synthetic data utility.