WorkDifferentWithAI.com Academic Paper Alert!
Written by Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, Ava J. E. Swevels
Category: AI for IT
Article Section: AI Development and Operations; MLOps and Model Management
Publication Date: 2024-01-23
SEO Description: Exploring Large Language Models’ effectiveness in explaining business processes with innovative SAX4BPM framework and user study insights.
Keywords
Large Language Models, Business Processes, Explainability, Situation-Aware eXplainability, Artificial Intelligence
AI-Generated Paper Summary
Generated by Ethical AI Researcher GPT
The paper titled “How well can large language models explain business processes?” by Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, and Ava J.E. Swevels investigates the integration of Large Language Models (LLMs) with business process management (BPM), focusing on the development of situation-aware explainability (SAX) explanations. The contribution includes the SAX4BPM framework, which utilizes a combination of services and a central knowledge repository to generate explanations that incorporate business process knowledge and causal relationships. The authors explore the augmentation of LLMs with business process-related views to produce improved and more trustworthy explanations. A significant portion of the research involves a methodological evaluation of the generated explanations’ quality via a designed scale and user study.
The backdrop for this investigation is the growing importance of LLMs across varied industries and functions, including their potential to automate explainability within business processes. Despite LLMs’ capabilities, there are concerns regarding their tendency for hallucinations and inadequate causal reasoning. The study aims to methodically enhance LLM-generated explanations by incorporating knowledge about business process definitions and causal executions as inputs. The paper discusses the prevailing concerns and opportunities surrounding LLM usage in BPM, proposing an innovative approach of blending different process-related views to fuel automatic generation of explanations through rigorous prompt engineering.
Author Caliber:
The authors hail from reputable institutions, with Dirk Fahland and Ava J.E. Swevels associated with Eindhoven University of Technology, Netherlands, and Fabiana Fournier, Lior Limonad, and Inna Skarbovsky affiliated with IBM Research, Haifa, Israel. Their collaboration, spanning academia and industry, brings a multidisciplinary perspective to the table, ensuring high levels of expertise and credibility in the fields of AI, BPM, and explainable AI (XAI).
Novelty & Merit:
- Development of the SAX4BPM framework for generating causally sound and human-interpretable business process explanations.
- Innovative integration of LLMs with a causal process execution view, a novel attempt at enhancing business process explainability through LLM prompt engineering.
- Methodological evaluation of LLM-generated explanations’ quality through a designated instrument and a structured user study.
Findings and Conclusions:
- Incorporating business process knowledge and causal relationships into LLM inputs enhances the perceived fidelity and trustworthiness of generated explanations.
- Improvement in explanation quality is moderated by users’ perception of trust and curiosity, indicating a balance between information quality and interpretability.
- The study highlights the potential of methodologically enhanced LLMs in BPM applications, alongside acknowledging inherent challenges such as hallucinations and causal reasoning.
Commercial Applications:
- Automated generation of business process explanations for BPM systems, improving process transparency and user trust.
- Development of LLM-enhanced tools and services for a variety of BPM applications, potentially extending beyond explanation generation to other process analysis and improvement tasks.
- Applications in hands-on training and educational tools for BPM and XAI, leveraging improved explanations to facilitate comprehension and user engagement.
Author’s Abstract
Large Language Models (LLMs) are likely to play a prominent role in future AI-augmented business process management systems (ABPMSs) catering functionalities across all system lifecycle stages. One such system’s functionality is Situation-Aware eXplainability (SAX), which relates to generating causally sound and yet human-interpretable explanations that take into account the process context in which the explained condition occurred. In this paper, we present the SAX4BPM framework developed to generate SAX explanations. The SAX4BPM suite consists of a set of services and a central knowledge repository. The functionality of these services is to elicit the various knowledge ingredients that underlie SAX explanations. A key innovative component among these ingredients is the causal process execution view. In this work, we integrate the framework with an LLM to leverage its power to synthesize the various input ingredients for the sake of improved SAX explanations. Since the use of LLMs for SAX is also accompanied by a certain degree of doubt related to its capacity to adequately fulfill SAX along with its tendency for hallucination and lack of inherent capacity to reason, we pursued a methodological evaluation of the quality of the generated explanations. To this aim, we developed a designated scale and conducted a rigorous user study. Our findings show that the input presented to the LLMs aided with the guard-railing of its performance, yielding SAX explanations having better-perceived fidelity. This improvement is moderated by the perception of trust and curiosity. More so, this improvement comes at the cost of the perceived interpretability of the explanation.