Chit-Chat or Deep Talk: Prompt Engineering for Process Mining
This work addresses process mining challenges for users needing conversational assistance, but it appears incremental as it builds on prior NLP research without claiming major breakthroughs.
The research tackled the complexity and skill requirements in process mining by applying Large Language Models (LLMs) to augment conversational agents, resulting in improved accessibility and agent performance as demonstrated in experiments on public datasets.
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.