Who Gets Unmasked? Sampler-Induced Bias in Diffusion Language Models
Diffusion language models (dLLMs) generate by gradually unmasking a canvas, so their samplers decide not only which token to write but also when to commit it. We show that this second decision changes who appears in generated text, a sampler-induced bias on top of the model's own. Common commit rules, including LLaDA's default, commit the positions whose sampled tokens are most probable; this peeking favors majority options. To separate the sampler from the model, we read the model's belief about a fork, such as a protagonist's gender, in one forward pass, and show that this belief matches exact sampling from the model. Measured against it, peeking samplers on LLaDA and Dream amplify occu...
| Authors | Xujun Che, Farsheed Haque, Depeng Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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