Summary of A Universal Prompting Strategy For Extracting Process Model Information From Natural Language Text Using Large Language Models, by Julian Neuberger et al.
A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models
by Julian Neuberger, Lars Ackermann, Han van der Aa, Stefan Jablonski
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of large language models (LLMs) in extracting information from textual process descriptions within the Business Process Management domain. Despite the advancements in natural language processing, existing methods still rely heavily on rule-based systems and machine learning approaches. The authors demonstrate that LLMs can outperform state-of-the-art machine learning approaches by up to 8% F1 score across three datasets using a novel prompting strategy. They also analyze the impact of different prompt parts on extraction quality, identifying key factors such as example texts, definition specificity, and format instructions for improving accuracy. The authors provide publicly available code, prompts, and data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can understand text descriptions of business processes. Right now, most methods use rules or machine learning, but this doesn’t work well with limited data. New language models are very good at understanding text without needing much training data. The authors tested these models and found that they could do better than existing approaches by up to 8%. They also looked at what makes the models work best. The results show that providing more examples, being specific about definitions, and following strict instructions helps get accurate information. The code, prompts, and data used in this research are available for everyone. |
Keywords
* Artificial intelligence * F1 score * Machine learning * Natural language processing * Prompt * Prompting
