Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models (2310.05793v2)

Published 9 Oct 2023 in cs.LG and cs.CL

Abstract: Diffusion models have gained prominence in generating high-quality sequences of text. Nevertheless, current approaches predominantly represent discrete text within a continuous diffusion space, which incurs substantial computational overhead during training and results in slower sampling speeds. In this paper, we introduce a soft absorbing state that facilitates the diffusion model in learning to reconstruct discrete mutations based on the underlying Gaussian space, thereby enhancing its capacity to recover conditional signals. During the sampling phase, we employ state-of-the-art ODE solvers within the continuous space to expedite the sampling process. Comprehensive experimental evaluations reveal that our proposed method effectively accelerates the training convergence by 4x and generates samples of similar quality 800x faster, rendering it significantly closer to practical application. \footnote{The code is released at \url{https://github.com/Shark-NLP/DiffuSeq}

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems.
  2. A cheaper and better diffusion language model with soft-masked noise. ArXiv, abs/2304.04746.
  3. Analog bits: Generating discrete data using diffusion models with self-conditioning. ArXiv, abs/2208.04202.
  4. Difformer: Empowering diffusion model on embedding space for text generation. ArXiv, abs/2212.09412.
  5. DiffuSeq: Sequence to sequence text generation with diffusion models. In International Conference on Learning Representations, ICLR.
  6. Efficient diffusion training via min-snr weighting strategy. ArXiv, abs/2303.09556.
  7. Diffusionbert: Improving generative masked language models with diffusion models. ArXiv, abs/2211.15029.
  8. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems.
  9. Argmax flows and multinomial diffusion: Learning categorical distributions. Advances in Neural Information Processing Systems.
  10. Unified discrete diffusion for simultaneous vision-language generation. ArXiv, abs/2211.14842.
  11. Philipp Koehn. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of the 2004 conference on empirical methods in natural language processing, pages 388–395.
  12. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871–7880.
  13. Diffusion-lm improves controllable text generation. ArXiv, abs/2205.14217.
  14. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out.
  15. Text generation with diffusion language models: A pre-training approach with continuous paragraph denoise. volume abs/2212.11685.
  16. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In Conference on Neural Information Processing Systems, NeurIPS.
  17. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. ArXiv, abs/2211.01095.
  18. Tess: Text-to-text self-conditioned simplex diffusion. ArXiv, abs/2305.08379.
  19. Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, ICML.
  20. Scaling neural machine translation. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 1–9. Association for Computational Linguistics.
  21. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, ACL.
  22. Denoising diffusion implicit models. In International Conference on Learning Representations, ICLR.
  23. Can diffusion model achieve better performance in text generation? bridging the gap between training and inference! ArXiv, abs/2305.04465.
  24. Seqdiffuseq: Text diffusion with encoder-decoder transformers. ArXiv, abs/2212.10325.
  25. Diffusum: Generation enhanced extractive summarization with diffusion. ArXiv, abs/2305.01735.
  26. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations, ICLR.
  27. A reparameterized discrete diffusion model for text generation. ArXiv, abs/2302.05737.
  28. Diffusion-nat: Self-prompting discrete diffusion for non-autoregressive text generation. ArXiv, abs/2305.04044.
  29. Texygen: A benchmarking platform for text generation models. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 1097–1100.
Citations (15)
View on Semantic Scholar

Summary

  • The paper introduces a soft absorbing state mechanism that removes MBR decoding and boosts model convergence.
  • It leverages advanced ODE solvers like DPM-solver++ to achieve text generation up to 800x faster than traditional methods.
  • Experimental results demonstrate a 4x improvement in training speed, effectively bridging discrete and continuous text representations.

Overview of "DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models"

The paper introduces DiffuSeq-v2, a novel approach to sequence-to-sequence (Seq2Seq) text generation using diffusion models. This work seeks to ameliorate some of the computational inefficiencies and speed constraints associated with existing diffusion models, which typically handle discrete text within a continuous space. DiffuSeq-v2 notably incorporates a soft absorbing state and leverages state-of-the-art Ordinary Differential Equation (ODE) solvers to achieve faster training and sampling processes.

Key Contributions

  1. Soft Absorbing State Mechanism: The authors propose a learned soft absorbing state to improve convergence speed and eliminate the necessity for Minimum Bayes Risk (MBR) decoding during sampling. This addition discretely modifies some elements of the sequence during the forward diffusion process.
  2. Expedited Sampling with ODE Solvers: By utilizing advanced ODE solvers like DPM-solver++, the sampling speed is significantly enhanced. This approach bypasses the typical need for extensive sampling iterations and rounds of decoding, achieving high-quality text generation with minimal steps.

Results and Implications

The experimental results presented in the paper demonstrate impressive acceleration in both training and sampling stages. The proposed method accelerates training convergence by 4x and enables text generation 800x faster compared to traditional methods. These improvements are significant for practical applications of Seq2Seq models, reducing the time and resources required for processing tasks such as machine translation and text summarization.

Numerical Findings

  • Training Speed and Convergence: The paper shows that the introduction of the absorbing state and discrete noises narrows the gap between diffusion models and auto-regressive models, leading to faster convergence by at least 1.75 times under certain configurations.
  • Quality and Efficiency Trade-offs: The new method circumvents the previous dependence on MBR decoding, which effectively doubles time consumption. Instead, it produces comparable results more efficiently, indicating a successful balance between quality and speed.

Theoretical and Practical Implications

Theoretically, this work contributes to bridging discrete and continuous representations in diffusion models, which could inspire further innovations in efficiently modeling discrete text data. Practically, the improvements in speed and resource efficiency make the adoption of diffusion models more feasible in real-world NLP applications.

Speculations and Future Directions

Going forward, the authors' approach could pave the way for enhancements in other applications requiring Seq2Seq translation, such as dialogue systems and content generation. Since this research indicates substantial improvements in handling discrete text data, future research might focus on integrating similar methodologies with more complex Seq2Seq tasks or exploring alternative ways to handle absorbing states to push the boundaries of diffusion-based text generation further.

In conclusion, DiffuSeq-v2 represents a significant stride towards overcoming the inherent limitations of diffusion models in text generation tasks, offering a promising avenue for future exploration and application in artificial intelligence research.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets