Recent advances in Large Language Models (LLMs) have shown impressive capabilities in various applications, yet LLMs face challenges such as limited context windows and difficulties in generalization. In this paper, we introduce a metacognition module for generative agents, enabling them to observe their own thought processes and actions. This metacognitive approach, designed to emulate System 1 and System 2 cognitive processes, allows agents to significantly enhance their performance by modifying their strategy. We tested the metacognition module on a variety of scenarios, including a situation where generative agents must survive a zombie apocalypse, and observe that our system outperform others, while agents adapt and improve their strategies to complete tasks over time.

翻译：近年来，大型语言模型的进步在各类应用中展现出令人瞩目的能力，然而它们仍面临上下文窗口有限和泛化困难等挑战。本文提出一种面向生成式代理的元认知模块，使其能够观察自身的思维过程与行为。这种模仿系统1与系统2认知过程的元认知方法，使代理能够通过调整策略显著提升性能。我们在包含生成式代理必须在丧尸末日情境中求生的多种场景下测试该模块，观察到我们的系统性能优于其他方案，且代理能够随时间推移适应并改进策略以完成任务。