用流程挖掘提升大模型技能学习，让计划生成更灵活可解释。
Skill Learning Using Process Mining for Large Language Model Plan Generation
- 用流程发现自动提取任务技能，无需人工标注。
- 通过流程模型存储技能，支持并行执行与动态调度。
- 结合合规检查实现精准技能检索，适合自动化决策场景。
大语言模型（LLMs）在生成复杂任务计划方面潜力巨大，但受限于串行执行、缺乏控制流建模及技能检索困难，影响其效率与可解释性。为此，我们提出一种基于流程挖掘的新型技能学习方法：利用流程发现进行技能获取，通过流程模型存储技能，并借助合规检查实现技能检索。该方法显著提升文本驱动的计划生成能力，支持灵活技能发现、并行执行与更高可解释性。实验表明，在特定条件下，我们的技能检索方法优于现有最先进基准，验证了其有效性。
原文摘要 · Abstract (English)
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.
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