闲聊还是深谈：面向流程挖掘的提示工程
Chit-Chat or Deep Talk: Prompt Engineering for Process Mining
AI总结：
本研究针对流程挖掘对话智能体的效率瓶颈，基于NLP对话智能体相关研究提出借助LLM的创新框架，经公开数据集验证可提升易用性与性能，为该领域后续研究奠定基础并指明方向。
摘要：
本研究探讨了大语言模型（Large Language Models, LLMs）在增强流程挖掘领域对话智能体方面的应用，旨在解决该领域固有的复杂性与多样技能要求问题。尽管LLM的发展为对话式流程挖掘带来了新机遇，但生成高效输出仍是一大障碍。受此前面向对话智能体的自然语言处理（Natural Language Processing, NLP）研究启发，我们提出了一种创新方法，可改善现有解决方案中的诸多问题。在公开问答数据集上的实验表明，我们的框架借助LLM同时提升了易用性与智能体性能。本研究为未来探索LLM在流程挖掘中的作用奠定了基础，文末还提出了增强LLM记忆、开展实时用户测试以及研究多样化数据集的建议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
