The Safety-Aware Denoiser for Text Diffusion Models
Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored. Existing safety approaches are geared toward autoregressive models and typically rely on post-hoc filtering or inference-time interventions. These are inadequate for effectively addressing safety risks in text diffusion models. We propose the Safety-Aware Denoiser (SAD), a safety-guidance framework in text diffusion models. The SAD modifies the iterative denoising process such that the text sample at the final denoising step is steered toward provably safe regions of the text space. This inference-time method can integrate safety constraints...
| Authors | Amman Yusuf, Zhejun Jiang, Mijung Park |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 8 August 2026 |
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