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arXiv · 2406.04959

Leveraging Generative AI for Extracting Process Models from Multimodal Documents

Marvin Voelter·Raheleh Hadian·Timotheus Kampik·Marius Breitmayer·Manfred Reichert
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Abstract

This paper presents an investigation of the capabilities of Generative Pre-trained Transformers (GPTs) to auto-generate graphical process models from multi-modal (i.e., text- and image-based) inputs. More precisely, we first introduce a small dataset as well as a set of evaluation metrics that allow for a ground truth-based evaluation of multi-modal process model generation capabilities. We then conduct an initial evaluation of commercial GPT capabilities using zero-, one-, and few-shot prompting strategies. Our results indicate that GPTs can be useful tools for semi-automated process modeling based on multi-modal inputs. More importantly, the dataset and evaluation metrics as well as the open-source evaluation code provide a structured framework for continued systematic evaluations moving forward.

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Marvin Voelter, Raheleh Hadian, Timotheus Kampik, Marius Breitmayer, Manfred Reichert. 2024-06-07. Leveraging Generative AI for Extracting Process Models from Multimodal Documents. https://arxiv.org/abs/2406.04959

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