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

     

Official implementation of our Buffer of Thoughts (BoT) framework (NeurIPS 2024 Spotlight). Affiliation: Peking University, UC Berkeley, Stanford University

Based on our BoT, we propose SuperCorrect, a novel two-stage fine-tuning method for improving both reasoning accuracy and self-correction ability for LLMs. Notably, the new SupperCorrect-7B model achieves new SOTA performance on MATH and GSM8K benchmarks among all 7B models.

📢 News

[2024.10] 🎉 We update our repo and release our implementation of Meta Buffer and Buffer Manager on math problems such as GSM8K based on amazing work light-RAG.

[2024.6] 🎉 We release our code for evaluation on three benchmarks.

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 and Inference 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. You may use our framework to construct your own meta buffer.

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. Inference on math problems

Here we provide our inference code of BoT based on light-RAG on GSM8K problems. We provide some thought templates about math problems in math.txt

from bot_pipeline import BoT
import argparse

parser = argparse.ArgumentParser(description='Use of argparse')

parser.add_argument('--llm_model',type=str,default='gpt-4o-mini',help='Model id of LLMs')
parser.add_argument('--embedding_model',type=str,default='text-embedding-3-large',help='Model id of embedding model')
parser.add_argument('--api_key',type=str,help='The api key of user')
parser.add_argument('--base_url',type=str,default='https://api.openai.com/v1/',help='we also support Open AI-like chat/embeddings APIs')
parser.add_argument('--rag_dir',type=str,default='./math',help='The path to save the meta buffer')

args = parser.parse_args()

llm_model = args.llm_model
embedding_model = args.embedding_model
api_key = args.api_key
base_url = args.base_url
rag_dir = args.rag_dir

prompt = "Solve the problem: Raymond and Samantha are cousins. Raymond was born 6 years before Samantha. Raymond had a son at the age of 23. If Samantha is now 31, how many years ago was Raymond's son born?"

bot = BoT(
          user_input= prompt, 
          api_key = api_key,
          model_id = llm_model,
          embedding_model = embedding_model,
          base_url = base_url,
          rag_dir = rag_dir
          )

bot.bot_inference()

Here you can use command below to conduct a quick test with OpenAI api using gpt-4o-mini and text-embedding-3-large by default

python inference.py --api_key 'Input your api key here'

You can also modify the problem within the prompt to solve different math problems.

(Our code is currently only support online LLMs on math problems, we will soon update and support local models !)

3.2. 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.

3.3. 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={Advances in Neural Information Processing Systems},
  year={2024}
}

Acknowledgements

The implementation of our Meta Buffer and Buffer Manager is based on light-RAG. We want to express our gratitude for their amazing work and we will continue to update and adapt more different RAG frameworks.