Abstract. Process Mining has come a long way to meet the needs of
organizations that must optimize their operations. However, its use is
still driven by technical users who can interpret process maps, models,
graphs and other types of analyses. Business users, on the other hand,
frequently report being intimidated by Process Mining tools’ interfaces
and not knowing “what to do next”. An alternative to address this issue is
providing more fluid and friendly interfaces for non-technical users based
on natural language querying. Recent advances in Large Language Models (LLMs) have expanded the horizon for such interfaces. In this work
we propose a new strategy to combine LLM capabilities with a framework for a natural language question-and-answer interface to Process
Mining, which combines the flexibility of the former with the scalability
and precision of the latter. We expand upon previous works in the area
to research the dimensions of flexibility, generalization, scalability and
precision. Finally, we implement such an LLM-enhanced framework and
test it against a real-life compilation of questions to compare the performance of LLM-based, non LLM-based and hybrid implementations and
point to directions in this field of research