Just Tell Me: Prompt Engineering in Business Process Management (2304.07183v1)

Abstract: GPT-3 and several other LLMs (LMs) can effectively address various 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.

Prompt Engineering in Business Process Management: Understanding Its Potentials and Challenges

The paper, "Just Tell Me: Prompt Engineering in Business Process Management," authored by Kiran Busch, Alexander Rochlitzer, Diana Sola, and Henrik Leopold, explores the novel area of applying prompt engineering in the domain of Business Process Management (BPM). The authors explore the feasibility, potential benefits, and inherent challenges associated with this approach to enhance the efficiency and effectiveness of NLP tasks within BPM.

Overview and Context

The rise of transformer-based LLMs (LMs), such as GPT-3 and BERT, has revolutionized several NLP tasks, including text summarization, machine translation, and reasoned responses to queries. Traditional approaches to leveraging these pre-trained models for task-specific applications typically involve fine-tuning. This process adapts a general-purpose LM to a specific task using large volumes of downstream data. However, within BPM, obtaining vast and high-quality labeled data can be challenging due to the need for domain-specific knowledge and privacy concerns.

Prompt engineering emerges as a powerful alternative, allowing pre-trained LMs to perform specific tasks by crafting effective natural language prompts during inference, without the need to modify the model itself. This approach circumvents the limitations associated with fine-tuning, such as the necessity of large task-specific datasets and extensive computational resources.

Potentials of Prompt Engineering in BPM

The paper outlines six key potentials of applying prompt engineering within BPM:

1. Effective Use of Limited Data Volumes: BPM tasks often suffer from the scarcity of large, high-quality labeled datasets. Prompt engineering, which integrates task specifications directly into input prompts, can deliver competitive performance even in low-data regimes. This makes it particularly suitable for BPM applications where ample labeled data is not readily available.
2. Natural Language-Based Interaction: Prompt engineering democratizes the use of sophisticated LMs by enabling interaction via intuitive natural language prompts. This ease of customization makes advanced LMs accessible to BPM practitioners irrespective of their technical backgrounds, allowing them to incorporate domain expertise directly into task specifications.
3. Input Optimization via Prompt Templates: By designing prompts that account for potential anomalies in input data, LMs can leverage their general-purpose knowledge to correct errors, enhancing the robustness of BPM applications such as predictive process monitoring and process transformations.
4. Overcoming Task Specificity: Traditional fine-tuning requires training a new model for each distinct task, leading to inefficiencies. Prompt engineering, however, allows a single LM to be versatile across numerous tasks, reducing the need for specialized models and enabling the development of more generalizable methods within BPM.
5. Improved Computational Efficiency: Fine-tuning large LMs is resource-intensive, imposing significant time and computational costs. Prompt engineering, in contrast, uses the existing pre-trained models more efficiently, which helps reduce the burden on computational resources and lowers the carbon footprint, supporting more sustainable practices.
6. Increased Explainability: Prompts offer a transparent medium to understand the task-specific behavior of LMs. They can decompose intricate tasks into intermediate steps, facilitating debugging and enhancing comprehensibility. This is crucial in BPM domains such as healthcare and finance, where decision-making needs to be transparent and justifiable.

Challenges in Realizing Prompt Engineering Potentials

Despite its promise, the application of prompt engineering within BPM is fraught with several challenges:

1. Process Representation in Prompts: BPM tasks often involve complex inputs such as process models or event logs, which are not straightforward to represent in natural language prompts. Developing effective prompt templates that capture the complexities of these representations remains a significant hurdle.
2. Limited Prompt Length: The input length constraints of LMs restrict the amount of context and instruction that can be provided within a single prompt. Selecting the most relevant information for a given task, while maintaining brevity, is a challenging balancing act.
3. Choice of Pre-Trained Model: Several pre-trained LMs exist, each with varying degrees of applicability to BPM tasks. The lack of benchmarks to systematically compare the process knowledge embedded in different LMs complicates the selection process.
4. Transferability of Prompts: The effectiveness of prompts can differ across various LMs. Ensuring that prompts are transferable and effective across models of different sizes and architectures requires further investigation.
5. Processing the Model Output: Converting the LM’s output back into a format suitable for BPM tasks often necessitates additional post-processing steps, which can be intricate and require domain-specific knowledge.
6. Evaluation of Prompt Templates: Systematically evaluating the efficacy of different prompt templates is resource-intensive but essential. Research is needed to develop efficient evaluation methodologies and design guidelines tailored to BPM.

Implications and Future Directions

The exploration of prompt engineering within BPM opens new avenues for leveraging advanced NLP capabilities in a more accessible and resource-efficient manner. The theoretical implications suggest a shift towards more versatile and generalizable ML practices within BPM. Practically, this approach can lower the barriers to employing sophisticated LMs, making advanced NLP tools available even to domains with limited data resources.

Future research should focus on addressing the outlined challenges, developing robust methodologies for prompt engineering, and creating benchmarks for evaluating the process knowledge in pre-trained LMs. Progress in these areas will likely enhance the practical utility and adaptability of LMs in BPM, driving more efficient and transparent decision-making processes.

In conclusion, this paper posits prompt engineering as a viable and potentially transformative methodology for BPM tasks, emphasizing both its promise and the critical hurdles that need to be overcome to fully realize its benefits.