LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models
This is an incremental survey that synthesizes existing research for the automated planning and NLP communities.
This survey addresses the challenge of using Large Language Models (LLMs) to formalize planning specifications for automated planning, aiming to enhance structured reasoning in long-horizon problems by reviewing current methodologies and identifying key challenges.
Large Language Models (LLMs) excel in various natural language tasks but often struggle with long-horizon planning problems requiring structured reasoning. This limitation has drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. However, identifying optimal AP deployment frameworks can be daunting and introduces new challenges. This paper aims to provide a timely survey of the current research with an in-depth analysis, positioning LLMs as tools for formalizing and refining planning specifications to support reliable off-the-shelf AP planners. By systematically reviewing the current state of research, we highlight methodologies, and identify critical challenges and future directions, hoping to contribute to the joint research on NLP and Automated Planning.