Despite the growing success of diffusion models in continuous-valued domains (e.g., images), similar efforts for discrete domains such as text have yet to match the performance of autoregressive language models. In this work, we present SSD-LM -- a diffusion-based language model with two key design choices. First, SSD-LM is semi-autoregressive, iteratively generating blocks of text, allowing for flexible output length at decoding time while enabling local bidirectional context updates. Second, it is simplex-based, performing diffusion on the natural vocabulary space rather than a learned latent space, allowing us to incorporate classifier guidance and modular control using off-the-shelf classifiers without any adaptation. We evaluate SSD-LM on unconstrained text generation benchmarks, and show that it matches or outperforms strong autoregressive GPT-2 models across standard quality and diversity metrics, while vastly outperforming diffusion-based baselines. On controlled text generation, SSD-LM also outperforms competitive baselines, with an extra advantage in modularity.

翻译：尽管扩散模型在连续域（如图像）中取得了日益增长的成功，但在文本等离散域中的类似努力尚未达到自回归语言模型的性能水平。本文提出SSD-LM——一种具有两个关键设计选择的扩散语言模型。首先，SSD-LM是半自回归的，通过迭代生成文本块，在解码时实现灵活的输出长度，同时支持局部双向上下文更新。其次，它基于单纯形，在自然词汇空间而非学习到的潜在空间上进行扩散，从而允许我们利用现成分类器直接集成分类器引导与模块化控制，无需任何适配。我们在无约束文本生成基准上评估SSD-LM，结果表明其在标准质量和多样性指标上与强自回归GPT-2模型相当或更优，同时大幅超越基于扩散的基线模型。在受控文本生成任务中，SSD-LM同样优于竞争基线，并在模块化方面具备额外优势。