How to build a diffusion language model

TL;DR

Diffusion language models (LLMs) have emerged as a competitive alternative to autoregressive models for text generation, enabling simultaneous sequence generation and iterative refinement. This advancement marks a significant shift in generative AI for discrete data, with practical implementations now available.

Key Takeaways

• Consider using masked diffusion models for improved text generation.
• Implement block diffusion for variable-length sequence generation.
• Utilize remasking to enable error correction in generated text.
• Evaluate model performance using perplexity to compare with autoregressive models.