

Buffer Of Thoughts
Overview :
Buffer of Thoughts (BoT) is a novel thought-augmentation reasoning method designed to enhance the accuracy, efficiency, and robustness of large language models (LLMs). It introduces a meta-buffer to store high-level thought templates extracted from problem-solving processes across various tasks, known as thought templates. For each problem, a relevant thought template is retrieved and adaptively instantiated into a specific reasoning structure for efficient reasoning. Furthermore, a buffer manager is proposed to dynamically update the meta-buffer, thus augmenting its capacity as more tasks are solved.
Target Users :
The target audience is researchers and developers who use large language models to solve complex problems. BoT enhances their efficiency and accuracy in utilizing LLMs for reasoning tasks, particularly in domains requiring handling large datasets and complex logic, by providing thought templates and dynamic buffer management.
Use Cases
Improving the accuracy of solving the '24-point game' using BoT
Rapidly finding solutions in the 'Checkmate-in-One' task using BoT
Performing the 'Word Sorting' task using BoT to optimize word sorting logic
Features
Stores and retrieves thought templates from problem-solving processes in a meta-buffer
Adaptively instantiates thought templates for efficient reasoning
Dynamically updates the meta-buffer to enhance problem-solving capabilities
Achieves significant performance improvements on multiple reasoning-intensive tasks
Compatible with various LLMs, such as GPT-4, Llama3-70B
Provides an easy-to-use command-line interface for quick testing and validation
How to Use
First, clone or download the Buffer of Thoughts code repository locally
Set up the environment, enter the project directory, and create a Python virtual environment, installing dependencies
Select a task, such as 'gameof24', and prepare the corresponding API keys and model ID
Run BoT via the command-line interface, inputting necessary parameters like task name, API keys, and model ID
Check the task data in the /benchmarks directory
Experimental results will be stored in the /test_results directory
Use the validate_results.py script to validate the test results and print the accuracy
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