@inproceedings{
ye2024diffusion,
title={Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models},
author={Jiacheng Ye and Shansan Gong and Liheng Chen and Lin Zheng and Jiahui Gao and Han Shi and Chuan Wu and Xin Jiang and Zhenguo Li and Wei Bi and Lingpeng Kong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=G0v0TxX01N}
}
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. 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.
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models
Jiacheng Ye Shansan Gong Liheng Chen Lin Zheng Jiahui Gao Han Shi Chuan Wu Xin Jiang Zhenguo Li Wei Bi Lingpeng Kong