mlsanityData to 7 Oct 2026

Just Tell Me: Prompt Engineering in Business Process Management

arXiv 2304.07183 ↗First posted 14 Apr 2023cs.AI cs.CL cs.LGcited by 17

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Abstract (arXiv)

GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

17 of 23 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  8. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  9. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  10. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  11. 2019Text Summarization with Pretrained Encoderscited by 553
  12. 2021True Few-Shot Learning with Language Modelscited by 343
5 more
  1. 2019Learning Deep Transformer Models for Machine Translationcited by 309
  2. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 216
  3. 2019Evaluating Commonsense in Pre-trained Language Modelscited by 95
  4. 2020Explainable Predictive Process Monitoringcited by 27
  5. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6

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Citing papers per year: 2023: 5; 2024: 7; 2025: 4; 2026: 1.

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  1. 2023Generative AIcited by 119
  2. 2023Large Language Models can accomplish Business Process Management Taskscited by 34
  3. 2024ProMoAI: Process Modeling with Generative AIcited by 12
  4. 2024MAO: A Framework for Process Model Generation with Multi-Agent Orchestrationcited by 8
  5. 2024Towards a Benchmark for Causal Business Process Reasoning with LLMscited by 7
  6. 2023Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?cited by 4
  7. 2023Large Process Models: A Vision for Business Process Management in the Age of Generative AIcited by 4
  8. 2023Cost-Efficient Prompt Engineering for Unsupervised Entity Resolutioncited by 4
  9. 2024NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methodscited by 3
  10. 2024Towards a Benchmark for Large Language Models for Business Process Management Taskscited by 3
  11. 2024Exploring the Potential of Conversational AI Support for Agent-Based Social Simulation Model Designcited by 2
  12. 2025Conversational Process Model Redesigncited by 2
5 more
  1. 2025BPMN Assistant: An LLM-Based Approach to Business Process Modelingcited by 2
  2. 2024Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methodscited by 1
  3. 2026Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Codecited by 1
  4. 2025LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documentscited by 0
  5. 2025Evaluation of LLMs for Process Model Analysis and Optimizationcited by 0