大型语言模型的K级推理

摘要

尽管大型语言模型（LLMs）已经展示了它们在复杂推理任务中的熟练表现，但它们在动态、互动和竞争场景中的表现——比如商业战略和股市分析——仍未得到充分探讨。为了弥补这一差距，我们正式探讨LLMs在快速演变环境中进行决策的动态推理能力。我们引入了两个基于博弈论的试点挑战，模拟了现实世界动态决策制定的复杂性。这些挑战定义明确，能够清晰、可控和精确地评估LLMs的动态推理能力。通过大量实验，我们发现现有的推理方法在需要k层思考的动态环境中往往表现不佳——这是之前研究未能解决的关键概念。为了解决这一问题，我们提出了一种新颖的LLMs推理方法，名为“K层推理”。该方法采用对手的视角，基于可用的历史信息递归地运用k层思考，显著提高了对手后续动作的预测准确性，并促进更具战略性的决策制定。这项研究不仅为评估动态推理设定了稳健的定量基准，还显著提升了LLMs在动态环境中的熟练程度。

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.

大型语言模型的K级推理

K-Level Reasoning with Large Language Models

摘要

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