Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

     

This repository contains the official implementation of our Buffer of Thoughts (BoT) framework. Affiliation: Peking University, UC Berkeley, Stanford University

🚩 New Updates

• Release initial code of BoT, supporting GPT-4 and Llama3-70B [2024.6.6]
• Update the code for smaller LLMs (e.g., Llama3-8B) [2024.6.24]
• Release meta-buffer and buffer-manager
• Extending BoT to more applications

Introduction

We introduce BoT, a novel and versatile thought-augmented reasoning approach designed to enhance the accuracy, efficiency, and robustness of large language models (LLMs). Specifically, we propose a meta-buffer to store a series of high-level thoughts, referred to as thought-templates, distilled from problem-solving processes across various tasks. For each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To ensure scalability and stability, we also propose a buffer-manager to dynamically update the meta-buffer, thus enhancing its capacity as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, achieving significant performance improvements over previous state-of-the-art (SOTA) methods: 11% on Game of 24, 20% on Geometric Shapes, and 51% on Checkmate-in-One. Further analysis demonstrates the superior generalization ability and robustness of our BoT, while requiring only 12% of the cost of multi-query prompting methods (e.g., tree/graph of thoughts) on average. Notably, we find that our Llama3-8B + BoT has the potential to surpass Llama3-70B model.

Overview of our BoT

Comparison between Different Methods

Task/Method	GPT-4	PAL	ToT	Meta Prompting	BoT (Ours)
Game of 24	3.0	64.0	74.0	67.0	82.4
MGSM (avg)	84.4	72.0	86.4	84.8	89.2
Multi-Step Arithmetic	84.0	87.4	88.2	90.0	99.8
WordSorting	80.4	93.2	96.4	99.6	100.0
Python Puzzles	31.1	47.3	43.5	45.8	52.4
Geometric Shapes	52.6	51.2	56.8	78.2	93.6
Checkmate-in-One	36.4	10.8	49.2	57.0	86.4
Date Understanding	68.4	76.2	78.6	79.2	88.2
Penguins	71.1	93.3	84.2	88.6	94.7
Sonnet Writing	62.0	36.2	68.4	79.6	80.0

Evaluation with Buffer of Thoughts

1. Benchmarks

For now, we release our demo version of BoT based on three different benchmarks:

• The Game of 24 from Yao et al., 2023
• Checkmate-in-One from the BIG-Bench suite (BIG-Bench authors, 2023)
• Word Sorting from BIG-Bench Hard (Suzgun et al., 2023; BIG-Bench authors, 2023)

2. Meta Buffer

For each task, we choose one thought template sampled from our meta-buffer library. Stay tuned for our complete meta-buffer library update!

3. Quick Start

First, set up the environment:

git clone https://github.com/YangLing0818/buffer-of-thought-llm
cd buffer-of-thought-llm
conda create -n BoT python==3.9 
pip install -r requirements.txt

3.1. Running on Three Benchmarks

Our BoT is easy to use. Just run:

python run_benchmarks.py --task_name 'gameof24' --api_key 'input your API key here if you want to use GPT-4' --model_id 'the model ID of GPT-4 or the path to your local LLM'

Here, --task_name could be one of gameof24, checkmate, wordsorting.

The --api_key is required if you want to use GPT-series; if not, you can skip it.

The --model_id should be the model ID of GPT-series like gpt-4o, gpt-4-turbo, or the path to your local LLM if you do not set --api_key.

The data for these three tasks are located in the /benchmarks directory.

The results generated during the experiment are stored in the /test_results directory.

3.2. Validate the Test Results

Run the command below to validate the test results of our BoT:

python validate_results.py --task_name 'gameof24' --test_path 'The path to the .jsonl file you want to validate'

This will print out the accuracy of the selected task on your relevant .jsonl file.

📖 BibTeX

@article{yang2024buffer,
  title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},
  author={Yang, Ling and Yu, Zhaochen and Zhang, Tianjun and Cao, Shiyi and Xu, Minkai and Zhang, Wentao and Gonzalez, Joseph E and Cui, Bin},
  journal={arXiv preprint arXiv:2406.04271},
  year={2024}
}