The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision.

翻译：大规模语言模型（LLMs）及其他生成式人工智能方法的持续成功，既彰显了大规模信息语料库相较于严格定义的符号模型所具有的优势，也证明了纯统计方法在安全性与可信度方面面临的挑战。作为理解LLMs及其他基础模型技术潜力与局限性的框架，我们提出了大型过程模型（LPM）的概念，该模型融合了LLMs的相关性分析能力与基于知识的系统及自动推理方法的分析精确性与可靠性。LPM旨在直接利用专家积累的丰富过程管理经验，以及具有不同特征（如规模、地区或行业）组织的流程绩效数据。在这一愿景中，LPM将使组织能够获得特定上下文（定制化）的过程模型及其他业务模型、深度分析洞察及改进建议。因此，它将大幅缩短业务转型所需的时间与精力，同时提供比以往更深入、更有效且更具可操作性的见解。我们认为LPM的实现是可行的，但也指出了需解决其具体方面时所存在的局限性及研究挑战。