Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
<|reference_start|>Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models: Recently, diffusion models have garnered significant interest in the field of text processing due to their many potential advantages compared to conventional autoregressive models. In this work, we propose Diffusion-of-Thought (DoT), a novel approach that integrates diffusion models with Chain-of-Thought, a well-established technique for improving the reasoning ability of autoregressive language models. In contrast to autoregressive language models that make decisions in a left-to-right, token-by-token manner, DoT allows reasoning steps to diffuse over time through a diffusion language model and offers greater flexibility in trading-off computation for reasoning performance. Our experimental results demonstrate the effectiveness of DoT in multi-digit multiplication, boolean logic, and grade school math problems, with a small diffusion model outperforming a much larger autoregressive model in both efficiency and accuracy. In addition to that, DoT showcases promising self-correction abilities and benefits from existing reasoning-enhancing techniques like self-consistency decoding. Our findings contribute to the understanding and development of reasoning with diffusion language models.<|reference_end|>
@article{ye2024diffusion,
    title={Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language
  Models},
    author={Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han
  Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong},
    journal={arXiv preprint arXiv:2402.07754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07754},
    primaryClass={cs.CL cs.AI cs.LG}
}