Infilling extraction on diffusion language models extracts up to three times more verbatim sequences than prefix methods and achieves higher recall on redacted emails than autoregressive models.
Diffusionbert: Improving generative masked language models with diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
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SHADOWMASK backdoors MDLMs by replacing the all-mask terminal distribution with a trigger-mask mixture prior, achieving near-100% attack success on DiT and LLaDA-8B models across multiple datasets while resisting fine-tuning and some defenses.
Analysis of Glauber dynamics on masked language models shows O(n log n) mixing under bounded cross-token influence and metastability with exponential escape times at low temperatures, plus empirical phase transitions.
citing papers explorer
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Extracting Training Data from Diffusion Language Models via Infilling
Infilling extraction on diffusion language models extracts up to three times more verbatim sequences than prefix methods and achieves higher recall on redacted emails than autoregressive models.
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Backdooring Masked Diffusion Language Models
SHADOWMASK backdoors MDLMs by replacing the all-mask terminal distribution with a trigger-mask mixture prior, achieving near-100% attack success on DiT and LLaDA-8B models across multiple datasets while resisting fine-tuning and some defenses.
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Mixing Times of Glauber Dynamics on Masked Language Models
Analysis of Glauber dynamics on masked language models shows O(n log n) mixing under bounded cross-token influence and metastability with exponential escape times at low temperatures, plus empirical phase transitions.