Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed planning. To address this, we take inspiration from the human brain, in which planning is accomplished via the recurrent interaction of specialized modules in the prefrontal cortex (PFC). These modules perform functions such as conflict monitoring, state prediction, state evaluation, task decomposition, and task coordination. We find that LLMs are sometimes capable of carrying out these functions in isolation, but struggle to autonomously coordinate them in the service of a goal. Therefore, we propose a black box architecture with multiple LLM-based (GPT-4) modules. The architecture improves planning through the interaction of specialized PFC-inspired modules that break down a larger problem into multiple brief automated calls to the LLM. We evaluate the combined architecture on three challenging planning tasks -- graph traversal, Tower of Hanoi, and logistics -- finding that it yields significant improvements over standard LLM methods (e.g., zero-shot prompting, in-context learning, and chain-of-thought). These results demonstrate the benefit of utilizing knowledge from cognitive neuroscience to improve planning in LLMs.

翻译：大语言模型（LLM）在多种任务上展现出令人瞩目的性能，但在需要多步推理或目标导向规划的任务中常常表现不佳。为解决这一问题，我们从人类大脑中汲取灵感——人类大脑中，规划过程是通过前额叶皮层（PFC）中专门模块的循环交互实现的。这些模块执行冲突监控、状态预测、状态评估、任务分解和任务协调等功能。我们发现，LLM有时能够单独执行这些功能，但难以自主协调它们以实现目标。因此，我们提出了一种具有多个基于LLM（GPT-4）模块的黑盒架构。该架构通过受PFC启发的专门模块之间的交互来改善规划能力：这些模块将大型问题分解为对LLM的多次简短自动调用。我们在三个具有挑战性的规划任务（图遍历、汉诺塔和物流规划）上评估了该组合架构，发现它相比标准LLM方法（如零样本提示、上下文学习和思维链）取得了显著改进。这些结果证明了利用认知神经科学知识来提升LLM规划能力的优势。