K-Level Reasoning with Large Language Models
- URL: http://arxiv.org/abs/2402.01521v1
- Date: Fri, 2 Feb 2024 16:07:05 GMT
- Title: K-Level Reasoning with Large Language Models
- Authors: Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Yan Xia, Man Lan, Furu
Wei
- Abstract summary: We explore the dynamic reasoning capabilities of Large Language Models (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.
- Score: 80.13817747270029
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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