Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which LLMs have been trained. However, it is impossible to provide a complete database or event log as an input prompt due to size constraints. In this paper, we apply LLMs in the context of process mining by i) abstracting the information of standard process mining artifacts and ii) describing the prompting strategies. We implement the proposed abstraction techniques into pm4py, an open-source process mining library. We present a case study using available event logs. Starting from different abstractions and analysis questions, we formulate prompts and evaluate the quality of the answers.

翻译：大语言模型（LLMs）能够以自然语言回答各种问题。随着近期进展（如GPT-4），LLMs在诸多专业性任务上已达到与人类相当的水平。业务流程分析可受益于自然过程查询语言以及LLM训练过程中积累的领域知识。然而，由于输入提示的规模限制，无法将完整数据库或事件日志作为输入。本文通过以下方式将LLMs应用于过程挖掘领域：i) 对标准过程挖掘工件的信息进行抽象，ii) 描述提示策略。我们将所提出的抽象技术实现于开源过程挖掘库pm4py中。通过使用公开事件日志开展案例研究，我们基于不同抽象和分析问题构建提示，并评估答案质量。