Evaluation of Large Language Models in Process Mining: Capabilities, Benchmarks, and Challenges

Core Concepts

The author discusses the essential capabilities of Large Language Models (LLMs) for Process Mining tasks and proposes benchmarks to evaluate their performance. The focus is on identifying key criteria for assessing LLMs' outputs in the context of Process Mining.

Abstract

The content delves into the significance of utilizing Large Language Models (LLMs) in Process Mining tasks. It highlights the importance of evaluating LLMs based on specific capabilities required for Process Mining, introducing benchmarks to measure their effectiveness. The paper emphasizes the need for comprehensive evaluation strategies to enhance confidence in using LLMs for Process Mining applications. The authors explore various aspects related to LLMs in Process Mining, including capabilities, benchmarks, and challenges. They provide insights into how LLMs can be leveraged for different PM tasks such as process description, modeling, anomaly detection, root cause analysis, fairness assessment, visual interpretation, and process improvement. Additionally, they propose strategies for evaluating LLM outputs through automatic assessment, human evaluation, and self-evaluation methods. Overall, the content serves as a valuable resource for researchers and practitioners interested in understanding the role of Large Language Models in Process Mining and offers a roadmap for evaluating their performance effectively.

Stats

Event logs challenge the context window limit of LLMs [15]. Visualizations like dotted charts are crucial for semi-automated PM [17]. Text-to-SQL capabilities are essential for analyzing event data [4]. Factuality ensures accurate information generation by LLMs [28].

Quotes

"The answer to these questions is fundamental to the development of comprehensive process mining benchmarks on LLMs covering different tasks and implementation paradigms." "LLMs may require additional knowledge about processes and databases to implement PM tasks."

Key Insights Distilled From

Evaluating Large Language Models in Process Mining

by Alessandro B... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06749.pdf

Deeper Inquiries

How can benchmarking strategies evolve to better assess the performance of LLMs in Process Mining?

Benchmarking strategies can evolve by incorporating more PM-specific benchmarks that focus on tasks unique to process mining. These benchmarks should cover a wide range of capabilities required for LLMs in PM, such as reasoning on visual prompts, hypothesis generation, and factuality checks. Additionally, creating dynamic and simulated benchmarks that reflect real-world PM scenarios can help evaluate LLMs' adaptability to unfamiliar data. Furthermore, developing extensive PM-specific benchmarks with annotated tasks and expected outcomes will enable a more comprehensive evaluation of LLM performance in process mining tasks.

What are potential challenges associated with training LLMs specifically tailored for PM tasks?

Training LLMs specifically tailored for PM tasks may pose several challenges. One major challenge is ensuring the novelty of input data during training to prevent overfitting on existing datasets like BPI challenge logs. Another challenge is evaluating the quality of these specialized models due to the lack of diverse and standardized benchmarks for PM applications. Moreover, fine-tuning LLMs with PM-specific information requires significant resources and constant updates to keep up with evolving processes and domain knowledge. Balancing between task specificity and generalizability while training these models is also crucial but challenging.

How can advancements in generative AI impact the future utilization of LLMs in Process Mining beyond current model capabilities?

Advancements in generative AI could significantly impact the future utilization of LLMs in Process Mining by enhancing their capabilities beyond what current models offer. For instance, extracting event logs from process execution videos using advanced vision models combined with language processing could revolutionize data extraction methods in process mining. Moreover, advancements in autonomous agents powered by large language models could lead to semi-autonomous or fully automated process analysis systems capable of handling complex analytical tasks without human intervention efficiently.