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when presented with such a task is often incorrect in the sense that following the output plan will
not actually solve the task. Therefore, in this work, we focus on resolving this issue by leveraging
the properties of classical planners. Similarly, some recent work also investigates approaches for
combining classical planning with LLMs [43, 44]. They either use prompting or ﬁne-tuning to make
LLMs capable of solving PDDL planning problems. In contrast, we do not solely rely on LLM as the
problem solver, but are more into taking the advantage of both the planner (i.e., generating accurate
and optimal plans) and the LLM itself (i.e., 1-shot generalization for translating natural-language
problem descriptions into PDDL).
LLM+P- Empowering Large Language Models with Optimal Planning Proficiency