Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration

Researchers propose DiSE, a self-evaluation method for diffusion large language models (dLLMs) that quantifies confidence by computing token regeneration probabilities. The method enables more efficient quality assessment and introduces a flexible-length generation framework that adaptively controls sequence length based on the model's self-assessment.