利用大型語言模型進行K層次推理

摘要

儘管大型語言模型（LLMs）已展示其在複雜推理任務中的優異表現，但它們在動態、互動和競爭場景（如商業策略和股市分析）中的表現仍未得到充分探索。為彌補這一差距，我們正式探索LLMs的動態推理能力，以在快速變化的環境中進行決策。我們引入了兩個基於博弈理論的試點挑戰，模擬現實世界動態決策的複雜性。這些挑戰定義明確，能夠清晰、可控和精確地評估LLMs的動態推理能力。通過大量實驗，我們發現現有的推理方法在需要k層思考的動態環境中往往表現不佳，這是先前研究未能解決的關鍵概念。為了應對這一問題，我們提出了一種新穎的LLMs推理方法，名為「K層推理」。該方法採用對手的觀點，根據可用的歷史信息遞歸地應用k層思考，顯著提高了對手後續動作的預測準確性，並促進更具戰略性的決策。這項研究不僅為評估動態推理設立了堅實的定量基準，還顯著提升了LLMs在動態情境中的表現水平。

English

While Large Language Models (LLMs) have demonstrated their proficiency in complex reasoning tasks, their performance in dynamic, interactive, and competitive scenarios - such as business strategy and stock market analysis - remains underexplored. To bridge this gap, we formally explore the dynamic reasoning capabilities of LLMs for decision-making in rapidly evolving environments. We introduce two game theory-based pilot challenges that mirror the complexities of real-world dynamic decision-making. These challenges are well-defined, enabling clear, controllable, and precise evaluation of LLMs' dynamic reasoning abilities. Through extensive experiments, we find that existing reasoning methods tend to falter in dynamic settings that require k-level thinking - a key concept not tackled by previous works. To address this, we propose a novel reasoning approach for LLMs, named "K-Level Reasoning". This approach adopts the perspective of rivals to recursively employ k-level thinking based on available historical information, which significantly improves the prediction accuracy of rivals' subsequent moves and informs more strategic decision-making. This research not only sets a robust quantitative benchmark for the assessment of dynamic reasoning but also markedly enhances the proficiency of LLMs in dynamic contexts.

利用大型語言模型進行K層次推理

K-Level Reasoning with Large Language Models

摘要

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