What are Text Diffusion Models? - An Overview

**What are Text Diffusion Models? - An Overview**

Introduction Text diffusion models are Large Language Models (LLMs) that generate text by using diffusion to “denoise” a set of generated tokens, instead of predicting one next token at a time as autoregressive (AR) LLMs do. Diffusion techniques are now common in image generation models such as Midjourney, but they have been less successful in language models so far, largely due to the differences in data types between image pixels and text. Text diffusion models have been getting more attention recently as some papers, such as the LLaDA and SEDD papers, have shown different kinds of text diffusion approaches to have the potential for faster, more accurate, and more flexible models in certain cases. This article explains the architectural differences, benefits, and potential use cases for text diffusion models. Key Takeaways The most successful text diffusion models so far use token masking, rather than Gaussian noise to predict output tokens iteratively in parallel...

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