Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation

We propose a new framework for zero-shot generation of synthetic tabular data. Using the large language model (LLM) GPT-4o and plain-language prompting, we demonstrate the ability to generate high-fidelity tabular data without task-specific fine-tuning or access to real-world data (RWD) for pre-training. To benchmark GPT-4o, we compared the fidelity and privacy of LLM-generated synthetic data against data generated with the conditional tabular generative adversarial network (CTGAN), across three open-access datasets: Iris, Fish Measurements, and Real Estate Valuation. Despite the zero-shot approach, GPT-4o outperformed CTGAN in preserving means, 95% confidence intervals, bivariate correlations, and data privacy of RWD, even at amplified sample sizes. Notably, correlations between parameters were consistently preserved with appropriate direction and strength. However, refinement is necessary to better retain distributional characteristics. These findings highlight the potential of LLMs in tabular data synthesis, offering an accessible alternative to generative adversarial networks and variational autoencoders.

Zero-shot generation of synthetic neurosurgical data with large language models

Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensive preprocessing and de-identification procedures. Synthetic data offers a potential solution to challenges associated with accessing and using real-world data (RWD). This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o, by benchmarking with the conditional tabular generative adversarial network (CTGAN). Synthetic datasets were compared to real-world neurosurgical data to assess fidelity (means, proportions, distributions, and bivariate correlations), utility (ML classifier performance on RWD), and privacy (duplication of records from RWD). The GPT-4o-generated datasets matched or exceeded CTGAN performance, despite no fine-tuning or access to RWD for pre-training. Datasets demonstrated high univariate and bivariate fidelity to RWD without directly exposing any real patient records, even at amplified sample size. Training an ML classifier on GPT-4o-generated data and testing on RWD for a binary prediction task showed an F1 score (0.706) with comparable performance to training on the CTGAN data (0.705) for predicting postoperative functional status deterioration. GPT-4o demonstrated a promising ability to generate high-fidelity synthetic neurosurgical data. These findings also indicate that data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes. Further investigation is necessary to improve the preservation of distributional characteristics and boost classifier performance.