Business Process Management (BPM) aims to improve organizational activities and their outcomes by managing the underlying processes. To achieve this, it is often necessary to consider information from various sources, including unstructured textual documents. Therefore, researchers have developed several BPM-specific solutions that extract information from textual documents using Natural Language Processing techniques. These solutions are specific to their respective tasks and cannot accomplish multiple process-related problems as a general-purpose instrument. However, in light of the recent emergence of Large Language Models (LLMs) with remarkable reasoning capabilities, such a general-purpose instrument with multiple applications now appears attainable. In this paper, we illustrate how LLMs can accomplish text-related BPM tasks by applying a specific LLM to three exemplary tasks: mining imperative process models from textual descriptions, mining declarative process models from textual descriptions, and assessing the suitability of process tasks from textual descriptions for robotic process automation. We show that, without extensive configuration or prompt engineering, LLMs perform comparably to or better than existing solutions and discuss implications for future BPM research as well as practical usage.

翻译：业务流程管理（BPM）旨在通过管理底层流程来改进组织活动及其成果。为实现这一目标，通常需要整合来自各类来源的信息，包括非结构化文本文档。因此，研究人员开发了多种BPM专用解决方案，利用自然语言处理技术从文本文档中提取信息。这些解决方案仅针对特定任务，无法作为通用工具解决多个流程相关问题。然而，随着近期具备卓越推理能力的大型语言模型（LLMs）的出现，这种具有多种应用潜力的通用工具已变得可行。本文通过将特定LLM应用于三个示范性任务——从文本描述中挖掘命令式流程模型、从文本描述中挖掘声明式流程模型，以及评估文本描述中流程任务对机器人流程自动化的适用性——展示了LLM如何完成文本相关的BPM任务。研究表明，在无需大量配置或提示工程的情况下，LLM的表现可与现有解决方案相当甚至更优，并讨论了其对未来BPM研究及实际应用的意义。