#88 · Primary category： Prompt Engineering Tools
tree-of-thought-prompting
Using Tree-of-Thought Prompting to boost ChatGPT's reasoning
Project last updated：12/09/23
GitHub Stars
820
Forks
76
Contributors
4
License
MIT
Why we included this project
Anyone who works with large language models has probably seen a Chain-of-Thought prompt return a confident but wrong answer. This repository shows a different approach: Tree-of-Thought prompting, which pushes the model to explore several reasoning branches, catch its own mistakes, and revise before settling on an answer. The author demonstrates it with a simple spatial puzzle that ChatGPT 3.5 gets wrong but answers correctly once the ToT prompt is applied, and includes a case where the technique brings a 3.5 model up to reasoning that previously needed GPT-4. The README also explains the ideas behind the technique clearly enough that you can adapt the prompt patterns to your own work instead of treating them as a black box. It's a short, practical reference for getting better reasoning out of models you already have.
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