On Planning Through LLMs

In recent years, various studies have been carried out to assess whether Large Language Models (LLMs) possess different reasoning capabilities, including those required in automated planning. Typically, these studies provide the LLM with a planning domain and a problem, specified by an initial state and a goal, and require the LLM model to generate a plan solving the problem. Despite this common configuration, such studies significantly differ in the used models, the information provided to the model, the possible involvement of symbolic planners, and the experimental approaches used for the evaluation. Motivated by the growing interest in LLMs and in the understanding of their reasoning abilities, in this work we offer a concise review of recent studies on using LLMs for planning. We outline the main research trends and discuss their most notable findings. Furthermore, we identify key challenges and highlight critical aspects to consider when evaluating a LLM in terms of learning to plan and generating solution plans.