Official Repo of Tree of Thoughts (ToT)

Official 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.

Setup

1. Set up OpenAI API key and store in environment variable OPENAI_API_KEY (see here).

2. Install tot package from source:

git clone https://github.com/princeton-nlp/tree-of-thought-llm
cd tree-of-thought-llm
# install the conda environment
conda create --name tree-of-thought-env python=3.12 -y
conda activate tree-of-thought-env
pip install -r requirements.txt
pip install -e .  # install `tot` package
source env.sh # set up the environment variables

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):

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)