  This technical report describes the intersection of process mining and large
language models (LLMs), specifically focusing on the abstraction of traditional
and object-centric process mining artifacts into textual format. We introduce
and explore various prompting strategies: direct answering, where the large
language model directly addresses user queries; multi-prompt answering, which
allows the model to incrementally build on the knowledge obtained through a
series of prompts; and the generation of database queries, facilitating the
validation of hypotheses against the original event log.
  Our assessment considers two large language models, GPT-4 and Google's Bard,
under various contextual scenarios across all prompting strategies. Results
indicate that these models exhibit a robust understanding of key process mining
abstractions, with notable proficiency in interpreting both declarative and
procedural process models.
  In addition, we find that both models demonstrate strong performance in the
object-centric setting, which could significantly propel the advancement of the
object-centric process mining discipline.
  Additionally, these models display a noteworthy capacity to evaluate various
concepts of fairness in process mining. This opens the door to more rapid and
efficient assessments of the fairness of process mining event logs, which has
significant implications for the field.
  The integration of these large language models into process mining
applications may open new avenues for exploration, innovation, and insight
generation in the field.
