# Large Process Models: A Vision for Business Process Management in the Age of Generative AI

arXiv: https://arxiv.org/abs/2309.00900 · HTML: https://mlsanity.com/paper/2309.00900/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 66 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [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)
- [Just Tell Me: Prompt Engineering in Business Process Management](https://mlsanity.com/paper/2304.07183/) (2023)
- [BINet: Multi-perspective Business Process Anomaly Classification](https://mlsanity.com/paper/1902.03155/) (2019)
- [Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning](https://mlsanity.com/paper/2103.11944/) (2021)
- [Mining Local Process Models](https://mlsanity.com/paper/1606.06066/) (2016)
- [Fairness-Aware Process Mining](https://mlsanity.com/paper/1908.11451/) (2019)
- [Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)](https://mlsanity.com/paper/2307.12701/) (2023)
- [On Observability and Monitoring of Distributed Systems: An Industry Interview Study](https://mlsanity.com/paper/1907.12240/) (2019)
- [Event Log Generation: An Industry Perspective](https://mlsanity.com/paper/2202.02539/) (2022)

## Built on by (most cited first, 4 of 4)

- [Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes](https://mlsanity.com/paper/2504.03693/) (2025)
- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery](https://mlsanity.com/paper/2604.22455/) (2026)

## Abstract (arXiv)

The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision.