Leveraging Generative AI for Extracting Process Models from Multimodal Documents
<|reference_start|>Leveraging Generative AI for Extracting Process Models from Multimodal Documents: 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.<|reference_end|>
@article{voelter2024leveraging,
    title={Leveraging Generative AI for Extracting Process Models from Multimodal
  Documents},
    author={Marvin Voelter, Raheleh Hadian, Timotheus Kampik, Marius Breitmayer,
  Manfred Reichert},
    journal={arXiv preprint arXiv:2406.04959},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04959},
    primaryClass={cs.SE}
}