https://github.com/princeton-nlp/tree-of-thought-llm

[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/princeton-nlp/tree-of-thought-llm

large-language-models llm prompting tree-of-thoughts tree-search

Last synced: 25 days ago
JSON representation

[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

• Host: GitHub
• URL: https://github.com/princeton-nlp/tree-of-thought-llm
• Owner: princeton-nlp
• License: mit
• Created: 2023-05-17T20:24:56.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-06-22T22:47:56.000Z (5 months ago)
• Last Synced: 2024-10-01T15:41:38.620Z (about 1 month ago)
• Topics: large-language-models, llm, prompting, tree-of-thoughts, tree-search
• Language: Python
• Homepage: https://arxiv.org/abs/2305.10601
• Size: 2.1 MB
• Stars: 4,651
• Watchers: 122
• Forks: 434
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• StarryDivineSky - princeton-nlp/tree-of-thought-llm

README

        
# Official Repo of Tree of Thoughts (ToT)



    

        

    

    

        

    

    

        

    

    

        

    



![teaser](pics/teaser.png)

Official implementation for paper [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) with code, prompts, model outputs.

Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/1659357547474681857) in 1min.

## Setup

1. Set up OpenAI API key and store in environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). 

2. Install `tot` package in two ways:

- Option 1: Install from PyPI

```bash

pip install tree-of-thoughts-llm

```

- Option 2: Install from source

```bash

git clone https://github.com/princeton-nlp/tree-of-thought-llm

cd tree-of-thought-llm

pip install -r requirements.txt

pip install -e .  # install `tot` package

```

## Quick Start

The following minimal script will attempt to solve the game of 24 with `4 5 6 10` (might be a bit slow as it's using GPT-4):

```python

import argparse

from tot.methods.bfs import solve

from tot.tasks.game24 import Game24Task

args = argparse.Namespace(backend='gpt-4', temperature=0.7, task='game24', naive_run=False, prompt_sample=None, method_generate='propose', method_evaluate='value', method_select='greedy', n_generate_sample=1, n_evaluate_sample=3, n_select_sample=5)

task = Game24Task()

ys, infos = solve(args, task, 900)

print(ys[0])

```

And the output would be something like (note it's not deterministic, and sometimes the output can be wrong):

```

10 - 4 = 6 (left: 5 6 6)

5 * 6 = 30 (left: 6 30)

30 - 6 = 24 (left: 24)

Answer: (5 * (10 - 4)) - 6 = 24

```

## Paper 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)

## Paper 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.

## How to Add A New Task

Setting up a new task is easy, and mainly involves two steps.

* Set up a new task class in ``tot/tasks/`` and task files in ``tot/data/``. See ``tot/tasks/game24.py`` for an example. Add the task to ``tot/tasks/__init__.py``.

* Set up task-specific prompts in ``tot/prompts/``. See ``tot/prompts/game24.py`` for an example. Depending on the nature of the task, choose ``--method_generate`` (choices=[``sample``, ``propose``]) and ``--method_evaluate`` (choices=[``value``, ``vote``]) and their corresponding prompts. 

## Citations

Please cite the paper and star this repo if you use ToT and find it interesting/useful, thanks! Feel free to contact [email protected] or open an issue if you have any questions.

```bibtex

@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}

}

```