The article discusses the concept of discrete language diffusion models, particularly how they relate to BERT and its masked language modeling technique. It explores the potential of fine-tuning BERT-like models for text generation by applying diffusion principles, highlighting the similarities between BERT's training objective and diffusion processes. The author references their exploration of this topic and mentions the existence of a related paper, DiffusionBERT, that provides more rigorous testing of the concepts presented.