Summary of A Knowledge-informed Large Language Model Framework For U.s. Nuclear Power Plant Shutdown Initiating Event Classification For Probabilistic Risk Assessment, by Min Xian et al.
A Knowledge-Informed Large Language Model Framework for U.S. Nuclear Power Plant Shutdown Initiating Event Classification for Probabilistic Risk Assessment
by Min Xian, Tao Wang, Sai Zhang, Fei Xu, Zhegang Ma
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a hybrid pipeline to identify and classify shutdown initiating events (SDIEs) in nuclear power plants using machine learning and large language models. The authors tackle challenges like imbalanced event types, label noise, and unavailable datasets by integrating knowledge-informed prescreening with a BERT-based large language model. The pipeline uses 44 SDIE text patterns to generate feature vectors for non-SDIEs, followed by fine-tuning the LLM on an SDIE dataset. Evaluation on a dataset of 10,928 events shows high accuracy and precision in classifying SDIEs into four types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps develop safer nuclear power plants by improving how we identify and classify events that can shut them down. Researchers use special computer programs to sort through lots of data and find important information. They came up with a new way to do this, combining two different approaches: one for quickly filtering out non-important events, and another for actually identifying the types of events. This new method works really well, accurately classifying almost 94% of shutdown initiating events. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Large language model » Machine learning » Precision
