  This research investigates the application of Large Language Models (LLMs) to
augment conversational agents in process mining, aiming to tackle its inherent
complexity and diverse skill requirements. While LLM advancements present novel
opportunities for conversational process mining, generating efficient outputs
is still a hurdle. We propose an innovative approach that amend many issues in
existing solutions, informed by prior research on Natural Language Processing
(NLP) for conversational agents. Leveraging LLMs, our framework improves both
accessibility and agent performance, as demonstrated by experiments on public
question and data sets. Our research sets the stage for future explorations
into LLMs' role in process mining and concludes with propositions for enhancing
LLM memory, implementing real-time user testing, and examining diverse data
sets.
