Group-wise Prompting for Synthetic Tabular Data Generation using Large Language Models

arXiv:2404.12404v1 Announce Type: new
Abstract: Generating realistic synthetic tabular data presents a critical challenge in machine learning. This study introduces a simple yet effective method employing Large Language Models (LLMs) tailored to generate synthetic data, specifically addressing data imbalance problems. We propose a novel group-wise prompting method in CSV-style formatting that leverages the in-context learning capabilities of LLMs to produce data that closely adheres to the specified requirements and characteristics of the target dataset. Moreover, our proposed random word replacement …

arxiv cs.ai cs.lg data language language models large language large language models prompting synthetic tabular tabular data type wise