Key Points
1. A novel thought-augmented reasoning method called Buffer of Thoughts (BoT) is proposed to enhance the accuracy, efficiency, and robustness of large language models (LLMs) across various reasoning tasks.
2. The method introduces a meta-buffer to store informative high-level thoughts or thought-templates distilled from the problem-solving processes across various tasks. These thought-templates are then adaptively instantiated with specific reasoning structures to conduct efficient reasoning, improving accuracy, efficiency, and model robustness.
3. The Buffer of Thoughts (BoT) method demonstrates significant performance improvements over previous state-of-the-art methods on various reasoning-intensive tasks, achieving accuracy improvements of 11% on Game of 24, 20% on Geometric Shapes, and 51% on Checkmate-in-One, while requiring only 12% of the cost of multi-query prompting methods on average.
4. The method addresses limitations of existing single-query and multi-query prompting methods by providing general and high-level guidelines or thoughts from previously-completed tasks, which are informative for improving efficiency and accuracy when solving similar problems.
5. The method introduces a problem distiller to extract critical task-specific information and relevant constraints, as well as a buffer manager to dynamically update the meta-buffer, enhancing the capacity of the meta-buffer as more tasks are solved.
6. The BoT framework is evaluated across diverse reasoning tasks and demonstrates superior accuracy, efficiency, and robustness, outperforming previous prompting methods and achieving a better trade-off between model size and performance.
7. The method achieves better reasoning accuracy, efficiency, and robustness, and demonstrates a better trade-off between model size and performance, outperforming previous methods across various tasks.
8. The BoT method introduces new evaluation metrics, such as success rate, to assess reasoning robustness and demonstrates outstanding robustness attributed to the generalization ability of the distilled thought-templates across different tasks.
9. The BoT method has the potential to surpass larger language models in reasoning abilities, diminishes the inference cost required by large language models when tackling complex problems, and opens future directions for integrating external resources and improving thought-template distillation for more complex tasks.
Summary
The paper introduces a novel and versatile thought-augmented reasoning approach called Buffer of Thoughts (BoT) to enhance the performance of large language models (LLMs) on various reasoning-intensive tasks.
The key components of the BoT approach are:
Performance of BoT
The paper reports that the BoT approach achieves significant performance improvements over previous state-of-the-art methods on a diverse set of 10 challenging reasoning-intensive tasks. Specifically, it shows 11% improvement on Game of 24, 20% on Geometric Shapes, and 51% on Checkmate-in-One, while requiring only 12% of the cost of multi-query prompting methods on average.
Advantages of BoT
The key advantages of BoT are:
Overall, the BoT approach represents a promising step towards developing more capable and versatile reasoning systems based on large language models.
Reference: https://arxiv.org/abs/2406.04271