用大模型自动分类核电站安全事件记录，提升识别效率。
Classification of Safety Events at Nuclear Sites using Large Language Models
- 用大模型分析核电站运行记录，自动区分安全与非安全事件。
- 通过提示词优化和评分机制，分类准确率显著提升。
- 适合核能安全管理、AI辅助决策领域的研究人员参考。
本文提出一种基于大语言模型（LLM）的机器学习分类器，用于将核电站的站点状态记录（Station Condition Records, SCRs）划分为与安全相关和非安全相关两类。主要目标是通过提升分类效率与准确性，增强现有手动审查流程。论文对标注的SCR数据集进行了实验，评估了分类器性能，并探索了多种提示词变体对LLM决策过程的影响。此外，引入了一种数值评分机制，可为安全事件分类提供更细致灵活的判断依据。该方法在核安全管理体系中具有创新意义，是一种可扩展的安全事件识别工具。
原文摘要 · Abstract (English)
This paper proposes the development of a Large Language Model (LLM) based machine learning classifier designed to categorize Station Condition Records (SCRs) at nuclear power stations into safety-related and non-safety-related categories. The primary objective is to augment the existing manual review process by enhancing the efficiency and accuracy of the safety classification process at nuclear stations. The paper discusses experiments performed to classify a labeled SCR dataset and evaluates the performance of the classifier. It explores the construction of several prompt variations and their observed effects on the LLM's decision-making process. Additionally, it introduces a numerical scoring mechanism that could offer a more nuanced and flexible approach to SCR safety classification. This method represents an innovative step in nuclear safety management, providing a scalable tool for the identification of safety events.
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