  Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming
increasingly essential, and initial approaches yield promising results.
However, little attention has been given to developing strategies for
evaluating and benchmarking the utility of incorporating LLMs into PM tasks.
This paper reviews the current implementations of LLMs in PM and reflects on
three different questions. 1) What is the minimal set of capabilities required
for PM on LLMs? 2) Which benchmark strategies help choose optimal LLMs for PM?
3) How do we evaluate the output of LLMs on specific PM tasks? The answer to
these questions is fundamental to the development of comprehensive process
mining benchmarks on LLMs covering different tasks and implementation
paradigms.
