tree-of-thought-llm/readme.md

Offical Repo of Tree of Thoughts (ToT)

Offical implementation for paper Tree of Thoughts: Deliberate Problem Solving with Large Language Models with code, prompts, model outputs. Also check its tweet thread in 1min.

Note: https://github.com/kyegomez/tree-of-thoughts is not the offical/correct implementation for the results in the paper. Please check https://github.com/ysymyth/tree-of-thought-llm/issues/17

Please cite the paper and star this repo if you use ToT and find it interesting/useful. Thanks!

@misc{yao2023tree,
      title={{Tree of Thoughts}: Deliberate Problem Solving with Large Language Models}, 
      author={Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
      year={2023},
      eprint={2305.10601},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Setup

You need to first have an OpenAI API key and store it in the environment variable OPENAI_API_KEY (see here). If you use custom base url, set it by environment variable OPENAI_API_BASE (e.g. https://api.openai.com/v1).

Package requirement: pip install openai backoff sympy numpy

Experiments

Run experiments via sh scripts/{game24, text, crosswords}/{standard_sampling, cot_sampling, bfs}.sh, except in crosswords we use a DFS algorithm for ToT, which can be run via scripts/crosswords/search_crosswords-dfs.ipynb.

The very simple run.py implements the ToT + BFS algorithm, as well as the naive IO/CoT sampling. Some key arguments:

• --naive_run: if True, run naive IO/CoT sampling instead of ToT + BFS.
• --prompt_sample (choices=[standard, cot]): sampling prompt
• --method_generate (choices=[sample, propose]): thought generator, whether to sample independent thoughts (used in Creative Writing) or propose sequential thoughts (used in Game of 24)
• --method_evaluate (choices=[value, vote]): state evaluator, whether to use the value states independently (used in Game of 24) or vote on states together (used in Creative Writing)
• --n_generate_sample: number of times to prompt for thought generation
• --n_evaluate_sample: number of times to prompt for state evaluation
• --n_select_sample: number of states to keep from each step (i.e. b in the paper's ToT + BFS algorithm)

Trajectories

logs/ contains all the trajectories from the paper's experiments, except for logs/game24/gpt-4_0.7_propose1_value3_greedy5_start900_end1000.json which was reproduced after the paper (as the original experiment was done in a notebook) and achieved a 69% score instead of the original 74% score due to randomness in GPT decoding. We hope to aggregate multiple runs in the future to account for sampling randomness and update the paper, but this shouldn't affect the main conclusions of the paper.

Questions

Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.