Dynamic Planning with a LLM
Gautier Dagan Frank Keller Alex Lascarides
While Large Language Models (LLMs) can solve many NLP tasks in zero-shot
settings, applications involving embodied agents remain problematic. In
particular, complex plans that require multi-step reasoning become difficult
and too costly as the context window grows. Planning requires understanding the
likely effects of one's actions and identifying whether the current environment
satisfies the goal state. While symbolic planners find optimal solutions
quickly, they require a complete and accurate representation of the planning
problem, severely limiting their use in practical scenarios. In contrast,
modern LLMs cope with noisy observations and high levels of uncertainty when
reasoning about a task. Our work presents LLM Dynamic Planner (LLM-DP): a
neuro-symbolic framework where an LLM works hand-in-hand with a traditional
planner to solve an embodied task. Given action-descriptions, LLM-DP solves
Alfworld faster and more efficiently than a naive LLM ReAct baseline.