Skill Learning Using Process Mining for Large Language Model Plan Generation

TL;DR

This paper presents a novel method that integrates process mining techniques into large language models to improve skill learning, plan generation, and interpretability for complex task automation.

Contribution

It introduces a new approach combining process discovery, modeling, and conformance checking to enhance skill acquisition and retrieval in LLMs.

Findings

Skill retrieval accuracy surpasses state-of-the-art baselines.

Enables flexible skill discovery and parallel execution.

Improves interpretability of plan generation.

Abstract

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.