LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.
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Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.