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.

翻译：以GPT-4为代表的大型语言模型（LLMs）已彻底改变了自然语言处理领域，展现出卓越的语言精通度与推理能力。然而，其在战略性多智能体决策环境中的应用仍受限于若干显著缺陷，包括数学推理能力薄弱、遵循指令困难以及倾向于生成错误信息。这些不足阻碍了LLMs在战略性与交互式任务中的表现，此类任务通常要求严格遵守精细的游戏规则、进行长期规划、在未知环境中探索以及预判对手行动。为克服这些障碍，本文提出了一种新型LLM智能体框架，该框架配备记忆模块与专用工具，旨在增强其战略决策能力。我们在多个具有重要经济意义的环境中部署了这些工具，特别是双边议价以及多智能体动态机制设计场景。我们采用量化指标来评估该框架在各类战略决策问题中的性能。研究结果表明，我们提出的增强框架显著提升了LLMs的战略决策能力。尽管我们指出了当前LLM模型固有的局限性，但通过针对性增强措施所展现的改进，为LLM在交互式环境中的应用未来发展指明了一条充满前景的道路。