思緒緩衝區：利用大型語言模型進行思緒增強推理

摘要

我們介紹了一種名為「思緒緩衝區」（Buffer of Thoughts，BoT）的新穎且多用途的思緒增強推理方法，用於提升大型語言模型（LLMs）的準確性、效率和韌性。具體而言，我們提出了元緩衝區，用於存儲一系列信息豐富的高層次思緒，即從各種任務的問題解決過程中提煉出的思緒模板。然後，對於每個問題，我們檢索相關的思緒模板，並適應性地將其與具體的推理結構相結合，以進行高效的推理。為了確保可擴展性和穩定性，我們進一步提出了緩衝區管理器，動態更新元緩衝區，從而隨著解決更多任務而增強元緩衝區的容量。我們在10個具有挑戰性的推理密集型任務上進行了大量實驗，並在過去的最先進方法上實珅性能改進：在24點遊戲上提高了11％，在幾何形狀上提高了20％，在一步將軍上提高了51％。進一步的分析表明，我們的BoT具有出色的泛化能力和模型韌性，平均僅需多查詢提示方法（例如，思緒樹/圖）成本的12％。值得注意的是，我們發現我們的Llama3-8B+BoT有潛力超越Llama3-70B模型。我們的項目可在以下網址找到：https://github.com/YangLing0818/buffer-of-thought-llm

English

We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Specifically, we propose meta-buffer to store a series of informative high-level thoughts, namely thought-template, distilled from the problem-solving processes across various tasks. Then for each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To guarantee the scalability and stability, we further propose buffer-manager to dynamically update the meta-buffer, thus enhancing the capacity of meta-buffer as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, and achieve significant performance improvements over previous SOTA methods: 11% on Game of 24, 20% on Geometric Shapes and 51% on Checkmate-in-One. Further analysis demonstrate the superior generalization ability and model 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. Our project is available at: https://github.com/YangLing0818/buffer-of-thought-llm

思緒緩衝區：利用大型語言模型進行思緒增強推理

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

摘要

Summary

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