# Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study

arXiv: https://arxiv.org/abs/2307.02194 · HTML: https://mlsanity.com/paper/2307.02194/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2023.

## Built on by (most cited first, 20 of 20)

- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [ProMoAI: Process Modeling with Generative AI](https://mlsanity.com/paper/2403.04327/) (2024)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [Re-Thinking Process Mining in the AI-Based Agents Era](https://mlsanity.com/paper/2408.07720/) (2024)
- [Leveraging Large Language Models for Enhanced Process Model Comprehension](https://mlsanity.com/paper/2408.08892/) (2024)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [LLM-based event abstraction and integration for IoT-sourced logs](https://mlsanity.com/paper/2409.03478/) (2024)
- [Automated Business Process Analysis: An LLM-Based Approach to Value Assessment](https://mlsanity.com/paper/2504.06600/) (2025)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Mining](https://mlsanity.com/paper/2508.16270/) (2025)
- [Skill Learning Using Process Mining for Large Language Model Plan Generation](https://mlsanity.com/paper/2410.12870/) (2024)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Revolutionizing Process Mining: A Novel Architecture for ChatGPT Integration and Enhanced User Experience through Optimized Prompt Engineering](https://mlsanity.com/paper/2405.10689/) (2024)
- [No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence](https://mlsanity.com/paper/2508.00116/) (2025)
- [LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines](https://mlsanity.com/paper/2606.09489/) (2026)
- [LLM-based Vulnerability Discovery in Business Process Documentation](https://mlsanity.com/paper/2608.04271/) (2026)
- [Automated Event Log Generation from Unstructured Text Using Finetuned LLMs](https://mlsanity.com/paper/2609.01320/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which LLMs have been trained. However, it is impossible to provide a complete database or event log as an input prompt due to size constraints. In this paper, we apply LLMs in the context of process mining by i) abstracting the information of standard process mining artifacts and ii) describing the prompting strategies. We implement the proposed abstraction techniques into pm4py, an open-source process mining library. We present a case study using available event logs. Starting from different abstractions and analysis questions, we formulate prompts and evaluate the quality of the answers.