  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.
