Large Language Models as Commonsense Knowledge for Large-Scale Task Planning
Main Conference Track
Authors: Zirui Zhao, Wee Sun Lee, David Hsu

Large-scale task planning is a major challenge.  Recent work exploits large  language models (LLMs) directly as a policy and shows surprisingly  interesting results.  This paper shows that LLMs provide a  commonsense model of the world in addition to a policy that acts on it.  The world model and the policy can be combined in a search  algorithm, such as Monte Carlo Tree Search (MCTS), to scale up task  planning.  In our new LLM-MCTS algorithm, the LLM-induced world model  provides a commonsense prior belief for MCTS to achieve effective reasoning;  the LLM-induced policy acts as a heuristic to guide the search, vastly  improving search efficiency. Experiments show that LLM-MCTS outperforms  both MCTS alone and policies induced by LLMs (GPT2 and GPT3.5) by a wide  margin, for complex, novel tasks.   Further experiments and analyses on multiple tasks --  multiplication, travel planning, object rearrangement --  suggest minimum description length (MDL)  as a general guiding principle: if the  description length of the world model is substantially smaller than that of  the  policy, using LLM as a world model for model-based planning is likely better  than using LLM solely as a policy.