Large Language Models can accomplish Business Process Management Tasks
Michael Grohs Luka Abb Nourhan Elsayed Jana-Rebecca Rehse
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.