SA-DiffuSeq: Sparse Attention for Scalable Long-Document Generation
Analysis
This paper introduces SA-DiffuSeq, a novel diffusion framework designed to tackle the computational challenges of long-document generation. By integrating sparse attention, the model significantly reduces computational complexity and memory overhead, making it more scalable for extended sequences. The introduction of a soft absorbing state tailored to sparse attention dynamics is a key innovation, stabilizing diffusion trajectories and improving sampling efficiency. The experimental results demonstrate that SA-DiffuSeq outperforms existing diffusion baselines in both training efficiency and sampling speed, particularly for long sequences. This research suggests that incorporating structured sparsity into diffusion models is a promising avenue for efficient and expressive long text generation, opening doors for applications like scientific writing and large-scale code generation.
Key Takeaways
“incorporating structured sparsity into diffusion models is a promising direction for efficient and expressive long text generation.”