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：大型语言模型（LLM）在流程挖掘任务中的应用日益重要，初步方法已取得显著成果。然而，目前鲜有关注如何制定将LLM融入流程挖掘任务的评估与基准测试策略。本文系统梳理了LLM在流程挖掘中的现有实现，并围绕三个不同问题进行反思：1）流程挖掘对LLM所需的最小能力集是什么？2）哪些基准策略有助于为流程挖掘选择最优LLM？3）如何评估LLM在特定流程挖掘任务中的输出质量？对这些问题的解答，对于构建覆盖不同任务与实现范式的LLM流程挖掘综合基准体系具有基础性意义。