Abstract
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.
M. Grohs, L. Abb, N. Elsayed—Equal contribution.
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Notes
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The corresponding confusion matrices can be found in our repository.
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Grohs, M., Abb, L., Elsayed, N., Rehse, JR. (2024). Large Language Models Can Accomplish Business Process Management Tasks. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_34
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