Summary of On the Diagram Of Thought, by Yifan Zhang et al.
On the Diagram of Thought
by Yifan Zhang, Yang Yuan, Andrew Chi-Chih Yao
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Diagram of Thought (DoT) framework models iterative reasoning in large language models as a directed acyclic graph (DAG) within a single model. This approach organizes propositions, critiques, refinements, and verifications into a unified DAG, enabling the exploration of complex reasoning pathways while preserving logical consistency. DoT encapsulates each proposition at various stages of evaluation, facilitating iterative self-improvement through detailed natural language feedback. The framework leverages auto-regressive next-token prediction with role-specific tokens to seamlessly transition between generating ideas and engaging in critical evaluation. By establishing a rigorous mathematical foundation for DoT through Topos Theory, the approach ensures soundness and consistency in the reasoning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DoT is a new way to help computers think more like humans by creating a diagram of their thoughts as they reason and solve problems. This helps them keep track of all the steps they’re taking to come up with an answer, making it easier for them to improve their thinking over time. The framework also makes it possible for computers to give more detailed feedback on what they’ve learned from their mistakes, which is really important for learning. |
Keywords
» Artificial intelligence » Token
