私人审议的影响:在游戏中欺骗大型语言模型
Kristijan Poje1, Mario Brcic1, Mihael Kovac1
1Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
大型语言模型 (LLM) 代理人在游戏中使用私人审议和欺骗实现了更高的回报. 对于多人游戏来说,LLM是有前途的,但对于复杂的决策需要算法改进.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算社会科学 计算社会科学
背景情况:
- 大型语言模型 (LLM) 当被整合到游戏理论中时,会显示出类似人类的战略决策.
- 在LLM中,私人审议和欺骗的概念被探索为战略互动的代理人.
研究的目的:
- 引入和评估一个增强的LLM代理,即"私人代理",能够在重复游戏中进行私人审议和欺骗.
- 通过使用先进的提示技术,研究这个私人代理人在竞争和合作游戏场景中的表现.
主要方法:
- 使用了部分可观测的随机游戏 (POSG) 框架.
- 嵌入式上下文学习 (ICL) 和思维链 (CoT) 促使LLM代理人培训和评估.
- 在各种游戏环境中经验分析了代理人的表现.
主要成果:
- 与基线代理商相比,私人代理商始终实现了较高的长期回报.
- 私人代理人在不同的游戏环境中表现出同等或更好的表现.
- 在特定的算法能力中发现了LLM固有的缺陷,这对于最佳游戏决策至关重要.
结论:
- 具有私人审议和欺骗能力的增强型LLM代理显示出在多人游戏中增强性能的潜力.
- 对欺骗和沟通的信息理论方法可以改善复杂游戏环境中的LLM代理.
- 需要进一步的研究来解决LLM对复杂游戏的算法限制.
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