SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control
Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov · 2023
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 offthe-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 diffusionbased baselines.On controlled text generation, SSD-LM also outperforms competitive baselines, with an extra advantage in modularity. 1