# How well can large language models explain business processes?

**Authors:** Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, Ava J. E. Swevels  
**arXiv:** [2401.12846](https://arxiv.org/abs/2401.12846)  
**Date:** 2024-01-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.12846v1/

## Key points

- Large Language Models (LLMs) have shown impressive performance in natural language processing tasks and prompt-based learning.
- The Situation-Aware eXplainability (SAX) framework leverages LLMs to provide causally sound and human-interpretable explanations within the context of business process executions.
- Business Process Management (BPM) is crucial for efficient movement towards organizational goals, with process discovery techniques analyzing event data to improve processes.
- Integrating LLMs in BPM systems holds promise for enhancing process understanding and decision-making.
- Incorporating LLMs in generating SAX explanations improves fidelity but may impact interpretability, highlighting potential benefits and challenges in utilizing LLMs in AI-augmented business process management systems.

## Summary

In this paper, we explore the potential of Large Language Models (LLMs) in enhancing AI-augmented business process management systems (ABPMSs) through the development of the Situation-Aware eXplainability (SAX) framework. LLMs are deep-learning models trained on extensive text data that have shown impressive performance in natural language processing tasks and prompt-based learning. The emerging field of prompt engineering focuses on effectively guiding LLM outputs for various tasks. Our SAX4BPM framework leverages LLMs to generate causally sound and human-interpretable explanations within the context of business process executions. By synthesizing textual input related to business processes, the LLM provides explanations for user inquiries about specific situations or conditions that may arise during process executions. Business Process Management (BPM) is crucial for ensuring efficient movement towards organizational goals. Process discovery techniques analyze event data to improve business processes by summarizing event logs into graph models representing activities and control-flow dependencies. The integration of LLMs in BPM systems holds promise for enhancing process understanding and decision-making. The structure of this paper includes a review of fundamental concepts, presentation of the SAX4BPM framework, an illustrative example showcasing its usage, evaluation of the tool's performance through a methodological approach and user study, experimental design details, results discussion, review of related work, and concluding remarks. Overall, our findings suggest that incorporating LLMs in generating SAX explanations improves fidelity but may impact interpretability. Through rigorous evaluation and user feedback, we highlight the potential benefits and challenges associated with utilizing LLMs in ABPMSs for enhanced explainability in business processes.