STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making

• URL: http://arxiv.org/abs/2405.16376v2
• Date: Tue, 28 May 2024 01:21:19 GMT
• Title: STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
• Authors: Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh Sharifi, Dirk Bergemann, Zhuoran Yang,
• Abstract summary: Large Language Models (LLMs) have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. This paper presents a novel framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities.
• Score: 43.734386326024016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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