  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.
