https://github.com/jieyilong/tree-of-thought-puzzle-solver

The Tree of Thoughts (ToT) framework for solving complex reasoning tasks using LLMs
https://github.com/jieyilong/tree-of-thought-puzzle-solver

artificial-intelligence autogpt chatgpt chatgpt-api llm machine-learning tree-of-thought tree-of-thoughts

Last synced: about 2 months ago
JSON representation

The Tree of Thoughts (ToT) framework for solving complex reasoning tasks using LLMs

• Host: GitHub
• URL: https://github.com/jieyilong/tree-of-thought-puzzle-solver
• Owner: jieyilong
• License: mit
• Created: 2023-04-16T03:53:12.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-23T02:15:14.000Z (5 months ago)
• Last Synced: 2024-08-23T03:27:09.238Z (5 months ago)
• Topics: artificial-intelligence, autogpt, chatgpt, chatgpt-api, llm, machine-learning, tree-of-thought, tree-of-thoughts
• Language: Python
• Homepage: https://arxiv.org/pdf/2305.08291.pdf
• Size: 103 KB
• Stars: 270
• Watchers: 4
• Forks: 34
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-ChatGPT-repositories - tree-of-thought-puzzle-solver - The Tree of Thoughts (ToT) framework for solving complex reasoning tasks using LLMs (NLP)

README

        
# Tree of Thought Puzzle Solver Demo

This repo implements a Sudoku puzzle solver based on our proposed Tree-of-Thought (ToT) framework, a novel approach aimed at improving the problem-solving capabilities of auto-regressive large language models (LLMs). The ToT technique is inspired by the human mind’s approach for solving complex reasoning tasks through trial and error. In this process, the human mind explores the solution space through a tree-like thought process, allowing for backtracking when necessary. To implement ToT as a software system, we augment an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. In order to solve a given problem, these modules engage in a multi-round conversation with the LLM. Unlike an auto-regressive LLM which generates a new token based on the preceding sequence of tokens without backward editing, the ToT framework allows the sytem to backtrack to the previous steps of the thought-process and explore other directions from there. For more details, please check out our preprint "Large Language Model Guided Tree-of-Thought":

https://arxiv.org/pdf/2305.08291.pdf

## Setup

Clone this repo and install the required dependencies (Python 3.9+ required):

```shell

git clone https://github.com/jieyilong/tree-of-thought-puzzle-solver

cd tree-of-thought-puzzle-solver

pip install -r requirements.txt

touch config.yaml

```

Edit the YAML file `config.yaml`, paste in the following content and save. Then, please set your choice of model (e.g. "gpt-3.5-turbo") and your OpenAI API Key:

```yaml

chatbot:

    type: "openai"

    max_context_length: 8000

    include_chat_history_in_query: false

openai:

    model: 

    api_key: 

```

## Run ToT

```shell

python run_tot.py ""

# Example

python run_tot.py "please solve this 4x4 sudoku puzzle [[*,1,*,*],[*,*,2,*],[*,*,*,4],[1,*,*,*]] where * represents a cell to be filled in."

```

## Run Experiments

```shell

# solver_type: zero_shot, one_shot_with_cot, few_shot_with_cot, tot

python run_expr.py  

# Example

python run_expr.py zero_shot data/benchmarks/sudoku/3x3_sudoku_puzzles.json

python run_expr.py one_shot_with_cot data/benchmarks/sudoku/3x3_sudoku_puzzles.json

python run_expr.py few_shot_with_cot data/benchmarks/sudoku/3x3_sudoku_puzzles.json

python run_expr.py tot data/benchmarks/sudoku/3x3_sudoku_puzzles.json

```

## Citation

```bibtex

@misc{long2023llmtot,

      title={Large Language Model Guided Tree-of-Thought}, 

      author={Jieyi Long},

      year={2023},

      eprint={2305.08291},

      archivePrefix={arXiv},

      primaryClass={cs.AI}

}

```