Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911935358828544 |
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| author | Berti, Alessandro Kourani, Humam Hafke, Hannes Li, Chiao-Yun Schuster, Daniel |
| author_facet | Berti, Alessandro Kourani, Humam Hafke, Hannes Li, Chiao-Yun Schuster, Daniel |
| contents | Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has been given to developing strategies for evaluating and benchmarking the utility of incorporating LLMs into PM tasks. This paper reviews the current implementations of LLMs in PM and reflects on three different questions. 1) What is the minimal set of capabilities required for PM on LLMs? 2) Which benchmark strategies help choose optimal LLMs for PM? 3) How do we evaluate the output of LLMs on specific PM tasks? The answer to these questions is fundamental to the development of comprehensive process mining benchmarks on LLMs covering different tasks and implementation paradigms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies Berti, Alessandro Kourani, Humam Hafke, Hannes Li, Chiao-Yun Schuster, Daniel Databases Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has been given to developing strategies for evaluating and benchmarking the utility of incorporating LLMs into PM tasks. This paper reviews the current implementations of LLMs in PM and reflects on three different questions. 1) What is the minimal set of capabilities required for PM on LLMs? 2) Which benchmark strategies help choose optimal LLMs for PM? 3) How do we evaluate the output of LLMs on specific PM tasks? The answer to these questions is fundamental to the development of comprehensive process mining benchmarks on LLMs covering different tasks and implementation paradigms. |
| title | Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies |
| topic | Databases |
| url | https://arxiv.org/abs/2403.06749 |