STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
<|reference_start|>STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making: Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampered by significant limitations including poor mathematical reasoning, difficulty in following instructions, and a tendency to generate incorrect information. These deficiencies hinder their performance in strategic and interactive tasks that demand adherence to nuanced game rules, long-term planning, exploration in unknown environments, and anticipation of opponents' moves. To overcome these obstacles, this paper presents a novel LLM agent framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities. We deploy the tools in a number of economically important environments, in particular bilateral bargaining and multi-agent and dynamic mechanism design. We employ quantitative metrics to assess the framework's performance in various strategic decision-making problems. Our findings establish that our enhanced framework significantly improves the strategic decision-making capability of LLMs. While we highlight the inherent limitations of current LLM models, we demonstrate the improvements through targeted enhancements, suggesting a promising direction for future developments in LLM applications for interactive environments.<|reference_end|>
@article{li2024stride:,
    title={STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and
  Interactive Decision-Making},
    author={Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh
  Sharifi, Dirk Bergemann, and Zhuoran Yang},
    journal={arXiv preprint arXiv:2405.16376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16376},
    primaryClass={cs.CL cs.GT}
}