# Large Language Models can accomplish Business Process Management Tasks

arXiv: https://arxiv.org/abs/2307.09923 · HTML: https://mlsanity.com/paper/2307.09923/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Large Language Models for Business Process Management: Opportunities and Challenges](https://mlsanity.com/paper/2304.04309/) (2023)
- [Just Tell Me: Prompt Engineering in Business Process Management](https://mlsanity.com/paper/2304.07183/) (2023)
- [PET: An Annotated Dataset for Process Extraction from Natural Language Text](https://mlsanity.com/paper/2203.04860/) (2022)
- [Conformance Checking Based on Multi-Perspective Declarative Process Models](https://mlsanity.com/paper/1503.04957/) (2015)
- [Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?](https://mlsanity.com/paper/2304.11065/) (2023)
- [Extracting Semantic Process Information from the Natural Language in Event Logs](https://mlsanity.com/paper/2103.11761/) (2021)

## Built on by (most cited first, 30 of 34)

- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [Automating the Enterprise with Foundation Models](https://mlsanity.com/paper/2405.03710/) (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)
- [RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data](https://mlsanity.com/paper/2505.19030/) (2025)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (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)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations](https://mlsanity.com/paper/2407.17478/) (2024)
- [Bridging Domain Knowledge and Process Discovery Using Large Language Models](https://mlsanity.com/paper/2408.17316/) (2024)
- [SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures](https://mlsanity.com/paper/2510.08942/) (2025)
- [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)
- [Towards a Benchmark for Large Language Models for Business Process Management Tasks](https://mlsanity.com/paper/2410.03255/) (2024)
- [Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?](https://mlsanity.com/paper/2507.08232/) (2025)
- [Structured Extraction from Business Process Diagrams Using Vision-Language Models](https://mlsanity.com/paper/2511.22448/) (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)
- [BPMN Assistant: An LLM-Based Approach to Business Process Modeling](https://mlsanity.com/paper/2509.24592/) (2025)
- [SAGE: A Service Agent Graph-guided Evaluation Benchmark](https://mlsanity.com/paper/2604.09285/) (2026)
- [In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks](https://mlsanity.com/paper/2604.27891/) (2026)
- [Skill Learning Using Process Mining for Large Language Model Plan Generation](https://mlsanity.com/paper/2410.12870/) (2024)
- [Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text](https://mlsanity.com/paper/2507.08362/) (2025)
- [What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models](https://mlsanity.com/paper/2507.11356/) (2025)
- [On LLM-Assisted Generation of Smart Contracts from Business Processes](https://mlsanity.com/paper/2507.23087/) (2025)
- [Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling](https://mlsanity.com/paper/2509.15336/) (2025)
- [Assessing the Business Process Modeling Competences of Large Language Models](https://mlsanity.com/paper/2601.21787/) (2026)
- [Evaluation of LLMs for Process Model Analysis and Optimization](https://mlsanity.com/paper/2510.07489/) (2025)
- [Evaluating LLM-Based Process Explanations under Progressive Behavioral-Input Reduction](https://mlsanity.com/paper/2510.09732/) (2025)
- [FM SO.P: A Progressive Task Mixture Framework with Automatic Evaluation for Cross-Domain SOP Understanding](https://mlsanity.com/paper/2602.09336/) (2026)

## Abstract (arXiv)

Business Process Management (BPM) aims to improve organizational activities and their outcomes by managing the underlying processes. To achieve this, it is often necessary to consider information from various sources, including unstructured textual documents. Therefore, researchers have developed several BPM-specific solutions that extract information from textual documents using Natural Language Processing techniques. These solutions are specific to their respective tasks and cannot accomplish multiple process-related problems as a general-purpose instrument. However, in light of the recent emergence of Large Language Models (LLMs) with remarkable reasoning capabilities, such a general-purpose instrument with multiple applications now appears attainable. In this paper, we illustrate how LLMs can accomplish text-related BPM tasks by applying a specific LLM to three exemplary tasks: mining imperative process models from textual descriptions, mining declarative process models from textual descriptions, and assessing the suitability of process tasks from textual descriptions for robotic process automation. We show that, without extensive configuration or prompt engineering, LLMs perform comparably to or better than existing solutions and discuss implications for future BPM research as well as practical usage.