Summary of Bridging Domain Knowledge and Process Discovery Using Large Language Models, by Ali Norouzifar et al.
Bridging Domain Knowledge and Process Discovery Using Large Language Models
by Ali Norouzifar, Humam Kourani, Marcus Dees, Wil van der Aalst
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper integrates Large Language Models (LLMs) into automated process discovery to leverage valuable domain knowledge from experts and documentation. This approach ensures alignment between discovered models and actual process executions by deriving rules from LLMs for model construction. The integration of LLMs bridges the gap between natural language process knowledge and robust process model discovery, advancing process analysis methodologies significantly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated process discovery helps identify good process models for tasks like conformance checking and improvements. But current methods often ignore valuable domain insights from experts and documentation. This paper uses Large Language Models to combine this knowledge with process discovery. It creates a bridge between natural language and robust process models, making it easier to analyze processes. |
Keywords
» Artificial intelligence » Alignment
