The article discusses the concept of using discrete language diffusion models for text generation, specifically highlighting how BERT's masked language modeling can be generalized into a diffusion framework. It explores the evolution from traditional models like BERT and GPT to the newer Gemini Diffusion model, and introduces the idea of transforming BERT's training objective into a generative process through variable masking rates. The author also notes the existence of related work, such as DiffusionBERT, which performs similar tasks with rigorous testing.