用于大型语言模型计划生成的基于流程挖掘的技能学习
Skill Learning Using Process Mining for Large Language Model Plan Generation
AI总结：
本文提出一种整合流程挖掘技术的LLM技能学习方法，利用流程发现、模型存储和一致性检查解决计划生成中的执行与检索难题，提升了生成效率、可解释性及检索准确率。
摘要：
大型语言模型（LLMs）在为复杂任务生成计划方面具有潜力，但其有效性受限于顺序执行、缺乏控制流模型以及技能检索困难。随着LLMs在自动化和决策中日益核心，解决这些问题对于提升计划生成的效率和可解释性至关重要。我们提出了一种通过整合流程挖掘技术进行LLMs技能学习的新方法：利用流程发现进行技能获取，利用流程模型进行技能存储，并利用一致性检查进行技能检索。该方法通过实现灵活的技能发现、并行执行和提升可解释性，增强了基于文本的计划生成。实验结果表明了该方法的有效性，在特定条件下，我们的技能检索方法超越了最先进的准确率基线。
英文摘要：
Large language models (LLMs) hold promise for generating plans for complex tasks, but their effectiveness is limited by sequential execution, lack of control flow models, and difficulties in skill retrieval. Addressing these issues is crucial for improving the efficiency and interpretability of plan generation as LLMs become more central to automation and decision-making. We introduce a novel approach to skill learning in LLMs by integrating process mining techniques, leveraging process discovery for skill acquisition, process models for skill storage, and conformance checking for skill retrieval. Our methods enhance text-based plan generation by enabling flexible skill discovery, parallel execution, and improved interpretability. Experimental results suggest the effectiveness of our approach, with our skill retrieval method surpassing state-of-the-art accuracy baselines under specific conditions.
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。
