In this paper, we address the challenges of managing Standard Operating Procedures (SOPs), which often suffer from inconsistencies in language, format, and execution, leading to operational inefficiencies. Traditional process modeling demands significant manual effort, domain expertise, and familiarity with complex languages like Business Process Modeling Notation (BPMN), creating barriers for non-techincal users. We introduce SOP Structuring (SOPStruct), a novel approach that leverages Large Language Models (LLMs) to transform SOPs into decision-tree-based structured representations. SOPStruct produces a standardized representation of SOPs across different domains, reduces cognitive load, and improves user comprehension by effectively capturing task dependencies and ensuring sequential integrity. Our approach enables leveraging the structured information to automate workflows as well as empower the human users. By organizing procedures into logical graphs, SOPStruct facilitates backtracking and error correction, offering a scalable solution for process optimization. We employ a novel evaluation framework, combining deterministic methods with the Planning Domain Definition Language (PDDL) to verify graph soundness, and non-deterministic assessment by an LLM to ensure completeness. We empirically validate the robustness of our LLM-based structured SOP representation methodology across SOPs from different domains and varying levels of complexity. Despite the current lack of automation readiness in many organizations, our research highlights the transformative potential of LLMs to streamline process modeling, paving the way for future advancements in automated procedure optimization.

翻译：本文针对标准操作程序管理中存在的语言、格式及执行不一致性问题展开研究，这些不一致性常导致运营效率低下。传统流程建模方法需要大量人工投入、领域专业知识以及对业务流程建模标注法等复杂语言的熟悉度，为非技术用户设置了使用门槛。我们提出SOP结构化方法——一种利用大语言模型将SOP转化为基于决策树的结构化表示的新方法。该方法能生成跨领域的标准化SOP表示，通过有效捕捉任务依赖关系并确保顺序完整性，降低认知负荷并提升用户理解度。我们的方法使得结构化信息既能用于工作流自动化，也能赋能人类用户。通过将程序组织为逻辑图，SOPStruct支持回溯与纠错机制，为流程优化提供了可扩展的解决方案。我们采用创新的评估框架，结合确定性方法与规划域定义语言来验证图结构的合理性，并利用大语言模型进行非确定性评估以确保完整性。通过跨领域、多复杂度层级的SOP实验，我们实证验证了基于大语言模型的结构化SOP表示方法的鲁棒性。尽管当前许多组织尚未具备自动化实施条件，但本研究揭示了大语言模型在简化流程建模方面的变革潜力，为自动化程序优化的未来发展开辟了道路。