STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
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arXiv
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| Format: | Preprint |
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2024
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| author | Li, Chuanhao Yang, Runhan Li, Tiankai Bafarassat, Milad Sharifi, Kourosh Bergemann, Dirk Yang, Zhuoran |
| author_facet | Li, Chuanhao Yang, Runhan Li, Tiankai Bafarassat, Milad Sharifi, Kourosh Bergemann, Dirk Yang, Zhuoran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making Li, Chuanhao Yang, Runhan Li, Tiankai Bafarassat, Milad Sharifi, Kourosh Bergemann, Dirk Yang, Zhuoran Computation and Language Computer Science and Game Theory 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. |
| title | STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making |
| topic | Computation and Language Computer Science and Game Theory |
| url | https://arxiv.org/abs/2405.16376 |