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.

翻译：将大型语言模型（LLMs）用于流程挖掘（PM）任务正变得日益重要，初步方法已展现出令人瞩目的成果。然而，关于制定评估策略和基准来衡量将LLMs融入PM任务效用的研究仍相对薄弱。本文综述了LLMs在PM领域的当前实现，并围绕三个问题展开反思：1）LLMs完成PM任务所需的最简能力集是什么？2）哪些基准策略有助于为PM选择最优LLMs？3）如何评估LLMs在特定PM任务上的输出？回答这些问题对于构建覆盖不同任务及实现范式的LLMs流程挖掘综合基准具有根本性意义。