  This is the first work to look at the application of large language models
(LLMs) for the purpose of model space edits in automated planning tasks. To set
the stage for this union, we explore two different flavors of model space
problems that have been studied in the AI planning literature and explore the
effect of an LLM on those tasks. We empirically demonstrate how the performance
of an LLM contrasts with combinatorial search (CS) -- an approach that has been
traditionally used to solve model space tasks in planning, both with the LLM in
the role of a standalone model space reasoner as well as in the role of a
statistical signal in concert with the CS approach as part of a two-stage
process. Our experiments show promising results suggesting further forays of
LLMs into the exciting world of model space reasoning for planning tasks in the
future.
