Discreteness in Diffusion LLMs: Challenges and Opportunities
Analysis
This paper analyzes the application of diffusion models to language generation, highlighting the challenges posed by the discrete nature of text. It identifies limitations in existing approaches and points towards future research directions for more coherent diffusion language models.
Key Takeaways
- •Diffusion models face challenges when applied to the discrete nature of text.
- •Existing approaches (continuous and discrete diffusion) have limitations.
- •Uniform corruption and token-wise training are identified as key issues.
- •The paper motivates research towards diffusion processes that better align with text structure.
Reference / Citation
View Original"Uniform corruption does not respect how information is distributed across positions, and token-wise marginal training cannot capture multi-token dependencies during parallel decoding."