Guided Diffusion for Trustworthy Synthetic Text Generation
In this paper, we propose a new paradigm for private synthetic text generation: Guided Diffusion.
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Key points
- 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.
- Leveraging the unique steerability of Diffusion Language Models, we introduce novel sampling and re-masking procedures that actively guide the iterative denoising steps.
- Empirically, we show that our approach generates high-quality synthetic text with precise semantic alignment.
Sources (1)
- [1]Guided Diffusion for Trustworthy Synthetic Text GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:27 PM
In this paper, we propose a new paradigm for private synthetic text generation: Guided Diffusion.
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.
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