K-Level Reasoning with Large Language Models
Yadong Zhang Shaoguang Mao Tao Ge Xun Wang Yan Xia Man Lan Furu Wei
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.