Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models

• URL: http://arxiv.org/abs/2402.07754v2
• Date: Mon, 15 Jul 2024 10:03:59 GMT
• Title: Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
• Authors: Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong,
• Abstract summary: Diffusion-of-Thought (DoT) is a novel approach that integrates diffusion models with Chain-of-Thought. DoT allows reasoning steps to diffuse over time through a diffusion language model. Our results demonstrate the effectiveness of DoT in multi-digit multiplication, logic, and grade school math problems.
• Score: 100.53662473219806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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