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Intrigued by the claims of emergent reasoning capabilities in LLMs trained ongeneral web corpora, in this paper, we set out to investigate their planningcapabilities. We aim to evaluate (1) how good LLMs are by themselves ingenerating and validating simple plans in commonsense planning tasks (of thetype that humans are generally quite good at) and (2) how good LLMs are inbeing a source of heuristic guidance for other agents--either AI planners orhuman planners--in their planning tasks. To investigate these questions in asystematic rather than anecdotal manner, we start by developing a benchmarksuite based on the kinds of domains employed in the International PlanningCompetition. On this benchmark, we evaluate LLMs in three modes: autonomous,heuristic and human-in-the-loop. Our results show that LLM's ability toautonomously generate executable plans is quite meager, averaging only about 3%success rate. The heuristic and human-in-the-loop modes show slightly morepromise. In addition to these results, we also make our benchmark andevaluation tools available to support investigations by research community.

On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)