  Generating plans of action, and reasoning about change have long been
considered a core competence of intelligent agents. It is thus no surprise that
evaluating the planning and reasoning capabilities of large language models
(LLMs) has become a hot topic of research. Most claims about LLM planning
capabilities are however based on common sense tasks-where it becomes hard to
tell whether LLMs are planning or merely retrieving from their vast world
knowledge. There is a strong need for systematic and extensible planning
benchmarks with sufficient diversity to evaluate whether LLMs have innate
planning capabilities. Motivated by this, we propose PlanBench, an extensible
benchmark suite based on the kinds of domains used in the automated planning
community, especially in the International Planning Competition, to test the
capabilities of LLMs in planning or reasoning about actions and change.
PlanBench provides sufficient diversity in both the task domains and the
specific planning capabilities. Our studies also show that on many critical
capabilities-including plan generation-LLM performance falls quite short, even
with the SOTA models. PlanBench can thus function as a useful marker of
progress of LLMs in planning and reasoning.
