  Since the rise of Large Language Models (LLMs) a couple of years ago,
researchers in metaheuristics (MHs) have wondered how to use their power in a
beneficial way within their algorithms. This paper introduces a novel approach
that leverages LLMs as pattern recognition tools to improve MHs. The resulting
hybrid method, tested in the context of a social network-based combinatorial
optimization problem, outperforms existing state-of-the-art approaches that
combine machine learning with MHs regarding the obtained solution quality. By
carefully designing prompts, we demonstrate that the output obtained from LLMs
can be used as problem knowledge, leading to improved results. Lastly, we
acknowledge LLMs' potential drawbacks and limitations and consider it essential
to examine them to advance this type of research further.
