Diffusion Language Model

Diffusion language models (DLMs) offer an alternative to traditional autoregressive language models by generating text through a denoising process, starting from random noise and iteratively refining it towards a coherent sequence. Current research focuses on improving DLM performance, particularly in addressing limitations like slower generation speeds and lower likelihoods compared to autoregressive counterparts, often through architectural innovations such as semi-autoregressive approaches and simplex-based methods, and by incorporating techniques like classifier guidance for controllable text generation. This research is significant because DLMs offer potential advantages in parallel generation, text interpolation, and control over specific text attributes, leading to improvements in various NLP tasks and applications like text summarization, machine translation, and even protein sequence generation.