# PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks

arXiv: https://arxiv.org/abs/2407.13244 · HTML: https://mlsanity.com/paper/2407.13244/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges](https://mlsanity.com/paper/2406.12624/) (2024)
- [Benchmarking Cognitive Biases in Large Language Models as Evaluators](https://mlsanity.com/paper/2309.17012/) (2023)
- [Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://mlsanity.com/paper/2402.14016/) (2024)
- [Can LLM be a Personalized Judge?](https://mlsanity.com/paper/2406.11657/) (2024)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Process Modeling With Large Language Models](https://mlsanity.com/paper/2403.07541/) (2024)
- [Large Language Models for Business Process Management: Opportunities and Challenges](https://mlsanity.com/paper/2304.04309/) (2023)
- [Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study](https://mlsanity.com/paper/2307.02194/) (2023)
- [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)
- [WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks](https://mlsanity.com/paper/2406.13264/) (2024)
- [Chit-Chat or Deep Talk: Prompt Engineering for Process Mining](https://mlsanity.com/paper/2307.09909/) (2023)
- [Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)](https://mlsanity.com/paper/2307.12701/) (2023)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [Ranking Large Language Models without Ground Truth](https://mlsanity.com/paper/2402.14860/) (2024)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [A Collection of Simulated Event Logs for Fairness Assessment in Process Mining](https://mlsanity.com/paper/2306.11453/) (2023)
- [Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies](https://mlsanity.com/paper/2403.06749/) (2024)

## Built on by (most cited first, 16 of 16)

- [PersoBench: Benchmarking Personalized Response Generation in Large Language Models](https://mlsanity.com/paper/2410.03198/) (2024)
- [Domain Adaptation of LLMs for Process Data](https://mlsanity.com/paper/2509.03161/) (2025)
- [SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures](https://mlsanity.com/paper/2510.08942/) (2025)
- [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)
- [SAGE: A Service Agent Graph-guided Evaluation Benchmark](https://mlsanity.com/paper/2604.09285/) (2026)
- [On LLM-Assisted Generation of Smart Contracts from Business Processes](https://mlsanity.com/paper/2507.23087/) (2025)
- [Assessing the Business Process Modeling Competences of Large Language Models](https://mlsanity.com/paper/2601.21787/) (2026)
- [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)
- [Evaluating LLM-Based Process Explanations under Progressive Behavioral-Input Reduction](https://mlsanity.com/paper/2510.09732/) (2025)
- [Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs](https://mlsanity.com/paper/2601.11468/) (2026)
- [Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution](https://mlsanity.com/paper/2607.14456/) (2026)
- [Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches](https://mlsanity.com/paper/2607.27797/) (2026)
- [PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces](https://mlsanity.com/paper/2609.19883/) (2026)
- [Goal-driven Variant Categorization](https://mlsanity.com/paper/2609.22475/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the competitive level of open-source LLMs in PM tasks is unknown. In this paper, we propose PM-LLM-Benchmark, the first comprehensive benchmark for PM focusing on domain knowledge (process-mining-specific and process-specific) and on different implementation strategies. We focus also on the challenges in creating such a benchmark, related to the public availability of the data and on evaluation biases by the LLMs. Overall, we observe that most of the considered LLMs can perform some process mining tasks at a satisfactory level, but tiny models that would run on edge devices are still inadequate. We also conclude that while the proposed benchmark is useful for identifying LLMs that are adequate for process mining tasks, further research is needed to overcome the evaluation biases and perform a more thorough ranking of the competitive LLMs.