How to build a diffusion language model

This article explores diffusion language models as an alternative to autoregressive models for discrete data like text. Autoregressive models generate tokens sequentially, leading to limitations such as no error correction, slow generation, and causal attention. Diffusion models, conversely, generate entire sequences at once, iteratively refining them. This approach offers advantages like adjustable speed-quality trade-offs, built-in error correction, and bidirectional context awareness, potentially leading to more intelligent models.