SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control
This work addresses the problem of improving text generation performance and control for AI researchers and practitioners, offering a novel diffusion-based approach that is competitive with autoregressive models.
The authors tackled the challenge of applying diffusion models to text generation, which had not matched autoregressive models, by introducing SSD-LM, a semi-autoregressive simplex-based diffusion language model that matches or outperforms GPT-2 on quality and diversity metrics and excels in controlled generation with modularity.
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.