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SA-DiffuSeq: Addressing Computational and Scalability Challenges in Long-Document Generation with Sparse Attention

Alexandros Christoforos, Chadbourne Davis

TL;DR

SA-DiffuSeq addresses the efficiency bottlenecks of long-form diffusion-based text generation by integrating structured sparsity and adaptive routing. It combines Longformer-style sparse attention, Mixture of Experts, and diffusion adaptations including a soft absorbing state and a joint denoising loss to stabilize training and accelerate sampling. Empirical results across Arxiv, HotpotQA, Commonsense, and QQP show consistent gains in long-range coherence, semantic accuracy, and speed over DiffuSeq and Longformer baselines, with notable robustness for sequences beyond 8k tokens. The approach demonstrates that incorporating structured sparsity into diffusion models yields scalable, expressive long-text generation suitable for scientific writing, large-scale code, and extended dialogues.

Abstract

Diffusion based approaches to long form text generation suffer from prohibitive computational cost and memory overhead as sequence length increases. We introduce SA-DiffuSeq, a diffusion framework that integrates sparse attention to fundamentally improve scalability for long document modeling. By selectively allocating attention within the diffusion process, SA-DiffuSeq significantly reduces computational complexity while maintaining semantic coherence and generation quality. A key component of our method is a soft absorbing state tailored to sparse attention dynamics, which stabilizes diffusion trajectories and accelerates sequence reconstruction. This design improves sampling efficiency and enhances precision in long range dependency modeling. Extensive experiments demonstrate that SA-DiffuSeq consistently surpasses state of the art diffusion baselines in both training efficiency and sampling speed, with especially strong gains on extended sequences. These properties make SA-DiffuSeq well suited for demanding long form applications such as scientific writing, large scale code generation, and multi turn long context dialogue. Overall, our results indicate that incorporating structured sparsity into diffusion models is a promising direction for efficient and expressive long text generation.

SA-DiffuSeq: Addressing Computational and Scalability Challenges in Long-Document Generation with Sparse Attention

TL;DR

SA-DiffuSeq addresses the efficiency bottlenecks of long-form diffusion-based text generation by integrating structured sparsity and adaptive routing. It combines Longformer-style sparse attention, Mixture of Experts, and diffusion adaptations including a soft absorbing state and a joint denoising loss to stabilize training and accelerate sampling. Empirical results across Arxiv, HotpotQA, Commonsense, and QQP show consistent gains in long-range coherence, semantic accuracy, and speed over DiffuSeq and Longformer baselines, with notable robustness for sequences beyond 8k tokens. The approach demonstrates that incorporating structured sparsity into diffusion models yields scalable, expressive long-text generation suitable for scientific writing, large-scale code, and extended dialogues.

Abstract

Diffusion based approaches to long form text generation suffer from prohibitive computational cost and memory overhead as sequence length increases. We introduce SA-DiffuSeq, a diffusion framework that integrates sparse attention to fundamentally improve scalability for long document modeling. By selectively allocating attention within the diffusion process, SA-DiffuSeq significantly reduces computational complexity while maintaining semantic coherence and generation quality. A key component of our method is a soft absorbing state tailored to sparse attention dynamics, which stabilizes diffusion trajectories and accelerates sequence reconstruction. This design improves sampling efficiency and enhances precision in long range dependency modeling. Extensive experiments demonstrate that SA-DiffuSeq consistently surpasses state of the art diffusion baselines in both training efficiency and sampling speed, with especially strong gains on extended sequences. These properties make SA-DiffuSeq well suited for demanding long form applications such as scientific writing, large scale code generation, and multi turn long context dialogue. Overall, our results indicate that incorporating structured sparsity into diffusion models is a promising direction for efficient and expressive long text generation.
Paper Structure (20 sections, 9 equations, 1 figure, 7 tables)

This paper contains 20 sections, 9 equations, 1 figure, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Integration of Sparse Attention Mechanism
  5. Dilated Sliding Window for Capturing Long-Range Dependencies
  6. Incorporation of Mixture of Experts
  7. Adaptation of Diffusion Processes
  8. Joint Denoising and Loss Optimization
  9. Consistency in Sampling and Inference
  10. Computational Efficiency and Scalability
  11. Experiments
  12. Results and Analyses
  13. Arxiv Abstract Dataset
  14. HotpotQA Dataset
  15. Commonsense Conversation Dataset
  16. ...and 5 more sections

Figures (1)

  • Figure 1: $(a)$ Text Embedding Generation, $(b)$ Noise Addition Across Layers, $(c)$ Sparse Attention Mechanism with Mixture of Experts.