对比九种流程模型表示法，发现Mermaid最适合大模型处理，BPMN文本生成效果最好。
What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models
- 构建包含55个流程描述与九种表示法的PMo数据集，首次系统评估不同表示法
- Mermaid在六项指标中综合得分最高，适合大模型任务；BPMN文本生成元素相似度最佳
- 揭示表示法结构复杂性与生成质量的关系，为流程建模提供实证依据
大型语言模型（LLMs）越来越多地应用于流程建模（PMo）任务，如流程模型生成（PMG）。为支持这些任务，研究者提出了多种流程模型表示法（PMRs），作为模型抽象或生成目标。然而，这些表示法在结构、复杂性和可用性方面差异显著，且从未被系统比较。此外，现有PMG方法采用不同的评估策略和生成技术，导致比较困难。本文首次提出针对LLM进行流程建模的实证研究，引入了新的PMo数据集，包含55个流程描述及其对应九种不同表示法的模型。我们从两个维度评估了各PMRs：对基于LLM的流程建模的适用性，以及在流程模型生成中的表现。结果表明，Mermaid在六个PMo评价标准中综合得分最高，而BPMN text在过程元素相似度方面取得最佳生成效果。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly applied for Process Modeling (PMo) tasks such as Process Model Generation (PMG). To support these tasks, researchers have introduced a variety of Process Model Representations (PMRs) that serve as model abstractions or generation targets. However, these PMRs differ widely in structure, complexity, and usability, and have never been systematically compared. Moreover, recent PMG approaches rely on distinct evaluation strategies and generation techniques, making comparison difficult. This paper presents the first empirical study that evaluates multiple PMRs in the context of PMo with LLMs. We introduce the PMo Dataset, a new dataset containing 55 process descriptions paired with models in nine different PMRs. We evaluate PMRs along two dimensions: suitability for LLM-based PMo and performance on PMG. \textit{Mermaid} achieves the highest overall score across six PMo criteria, whereas \textit{BPMN text} delivers the best PMG results in terms of process element similarity.
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