用大模型分析流程图，能自动发现逻辑错误并深度推理。
Evaluation of LLMs for Process Model Analysis and Optimization
- 通过自然语言交互让大模型理解流程图，无需额外训练。
- 零样本下可识别流程图的语法、逻辑与语义错误。
- 适合流程设计者使用，能模拟人类思维进行优化建议。
本文报告了我们对多种大语言模型在交互式自然语言界面下理解流程模型的能力评估。研究聚焦于模型从图像中解析BPMN流程图、回答多层级问题（语法、逻辑、语义）以及进行深层推理的能力。结果显示，未经训练的ChatGPT（o3模型）在零样本设置下已能有效理解流程图并智能应答。不同模型在准确性和效率上表现各异，但总体表明大模型可在流程设计与分析中发挥重要辅助作用。此外，我们探究了模型的“思考过程”，发现其在流程分析与优化中展现出类人推理特征。
原文摘要 · Abstract (English)
In this paper, we report our experience with several LLMs for their ability to understand a process model in an interactive, conversational style, find syntactical and logical errors in it, and reason with it in depth through a natural language (NL) interface. Our findings show that a vanilla, untrained LLM like ChatGPT (model o3) in a zero-shot setting is effective in understanding BPMN process models from images and answering queries about them intelligently at syntactic, logic, and semantic levels of depth. Further, different LLMs vary in performance in terms of their accuracy and effectiveness. Nevertheless, our empirical analysis shows that LLMs can play a valuable role as assistants for business process designers and users. We also study the LLM's "thought process" and ability to perform deeper reasoning in the context of process analysis and optimization. We find that the LLMs seem to exhibit anthropomorphic properties.
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