Summary of Diffusion Of Thoughts: Chain-of-thought Reasoning in Diffusion Language Models, by Jiacheng Ye et al.
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
by Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Diffusion-of-Thought (DoT) approach integrates diffusion models with Chain-of-Thought, a technique for improving the reasoning ability of autoregressive language models. This novel method allows for more flexible trading-off between computation and performance by diffusing reasoning steps over time through a diffusion language model. Experimental results demonstrate the effectiveness of DoT in multi-digit multiplication, boolean logic, and grade school math problems, outperforming larger autoregressive models in efficiency and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DoT is a new way to make computers reason like humans. It’s like taking small steps to solve a problem, rather than looking at each step one by one. This approach helps computers learn from their mistakes and get better at solving problems. In this paper, scientists tested DoT on simple math problems and found it was much faster and accurate than other methods. |
Keywords
* Artificial intelligence * Autoregressive * Diffusion * Language model
