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.
翻译:在商业流程管理中,只告诉我:提示工程
GPT-3 和其他几个语言模型(LM)可以有效地处理各种自然语言处理(NLP)任务,包括机器翻译和文本摘要。最近,在商业流程管理(BPM)领域成功地使用了它们,例如预测性流程监控和从文本中提取流程。然而,这通常需要对使用的 LM 进行微调,其中包括大量适合的训练数据。解决此问题的一个可能的方法是使用提示工程,它利用预先训练的 LM 而无需对其进行微调。认识到这一点,我们认为提示工程可以帮助将 LM 的能力引入 BPM 研究。我们使用这篇论文开发研究议程,通过识别相关的潜力和挑战,为使用提示工程进行 BPM 研究。