Guided Diffusion for Trustworthy Synthetic Text Generation↗ (원문, 새 창)

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arXiv:2610.27259v1 Announce Type: new Abstract: The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly output sensitive training data. Synthetic text generation offers a promising solution to this challenge; however, existing methods struggle to reconcile rigorous privacy guarantees with high data utility. In this paper, we propose a new paradigm for private synthetic text g