Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysis

TL;DR

This paper evaluates 16 large language models on business process modeling tasks, introduces a benchmark, and explores self-improvement methods, revealing performance variations and potential for quality enhancement through output optimization.

Contribution

It provides a comprehensive benchmark and analysis of LLMs in BPM, along with novel insights into self-improvement strategies like output optimization.

Findings

Significant performance variation among LLMs

Positive correlation between error handling and model quality

Output optimization improves low-performing models

Abstract

Large Language Models (LLMs) are rapidly transforming various fields, and their potential in Business Process Management (BPM) is substantial. This paper assesses the capabilities of LLMs on business process modeling using a framework for automating this task, a comprehensive benchmark, and an analysis of LLM self-improvement strategies. We present a comprehensive evaluation of 16 state-of-the-art LLMs from major AI vendors using a custom-designed benchmark of 20 diverse business processes. Our analysis highlights significant performance variations across LLMs and reveals a positive correlation between efficient error handling and the quality of generated models. It also shows consistent performance trends within similar LLM groups. Furthermore, we investigate LLM self-improvement techniques, encompassing self-evaluation, input optimization, and output optimization. Our findings indicate that output optimization, in particular, offers promising potential for enhancing quality, especially in models with initially lower performance. Our contributions provide insights for leveraging LLMs in BPM, paving the way for more advanced and automated process modeling techniques.