Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and Google's Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.

翻译：业务流程通常通过建模语言来表示，例如事件驱动流程链（EPC）、另一种工作流语言（YAWL）以及最流行的业务流程建模标准符号——业务流程模型与符号（BPMN）。近年来，聊天机器人——允许用户通过自然语言与机器交互的程序——越来越多地被用于支持业务流程执行。其中值得关注的一类聊天机器人是基于生成式的聊天机器人，它们由大语言模型（LLMs）驱动，例如OpenAI的生成式预训练Transformer（GPT）模型和Google的Pathways语言模型（PaLM）。这些模型基于数十亿参数进行训练，并支持对话智能。然而，目前尚不清楚基于生成式的聊天机器人是否能够理解并满足诸如BPMN所提供的流程执行支持构件的需求。本文通过案例研究，比较了两种主流生成式模型GPT和PaLM在流程执行支持场景中的性能。该研究揭示了利用生成式聊天机器人支持的对话方式来理解流程感知建模符号并帮助用户执行任务这一具有挑战性的问题。