Diffusion Language models (DLMs) are a promising avenue for text generation due to their practical properties on tractable controllable generation. They also have the advantage of not having to predict text autoregressively. However, despite these notable features, DLMs have not yet reached the performance levels of their autoregressive counterparts. One of the ways to reduce the performance gap between these two types of language models is to speed up the generation of DLMs. Therefore, we propose a novel methodology to address this issue in this work. It enables the execution of more generation steps within a given time frame, leading to higher-quality outputs. Specifically, our methods estimate DLMs completeness of text generation and allow adaptive halting of the generation process. We evaluate our methods on Plaid, SSD, and CDCD DLMs and create a cohesive perspective on their generation workflows. Finally, we confirm that our methods allow halting these models and decrease the generation time by $10$-$40$\% without a drop in the quality of model samples.

翻译：扩散语言模型因其在可控生成方面的实用特性而成为文本生成领域的一类有前途的方法，同时它们具有无需自回归预测文本的优势。然而，尽管具备这些显著特征，扩散语言模型的性能尚未达到其自回归对应模型的水平。缩小这两类语言模型性能差距的途径之一是加速扩散语言模型的生成过程。为此，本文提出了一种创新方法论以解决该问题。该方法可在给定时间框架内执行更多生成步骤，从而获得更高质量的生成结果。具体而言，我们的方法通过评估扩散语言模型文本生成的完整度，允许对生成过程进行自适应提前终止。我们在Plaid、SSD和CDCD三类扩散语言模型上评估了该方法，并建立了其生成流程的统一视角。最终验证表明，本文方法可在不降低模型样本质量的前提下，使生成时间缩短10%至40%。