{"title":"Impact of Generative AI (Large Language Models) on the PRA model construction and maintenance, observations","authors":"Valentin RychkovEDF R\\&D, Claudia PicocoEDF R\\&D, Emilie CalecaEDF R\\&D","doi":"arxiv-2406.01133","DOIUrl":null,"url":null,"abstract":"The rapid development of Large Language Models (LLMs) and Generative\nPre-Trained Transformers(GPTs) in the field of Generative Artificial\nIntelligence (AI) can significantly impact task automation in themodern\neconomy. We anticipate that the PRA field will inevitably be affected by this\ntechnology1. Thus, themain goal of this paper is to engage the risk assessment\ncommunity into a discussion of benefits anddrawbacks of this technology for\nPRA. We make a preliminary analysis of possible application of LLM\ninProbabilistic Risk Assessment (PRA) modeling context referring to the ongoing\nexperience in softwareengineering field. We explore potential application\nscenarios and the necessary conditions for controlledLLM usage in PRA modeling\n(whether static or dynamic). Additionally, we consider the potential impact\nofthis technology on PRA modeling tools.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Performance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.01133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid development of Large Language Models (LLMs) and Generative
Pre-Trained Transformers(GPTs) in the field of Generative Artificial
Intelligence (AI) can significantly impact task automation in themodern
economy. We anticipate that the PRA field will inevitably be affected by this
technology1. Thus, themain goal of this paper is to engage the risk assessment
community into a discussion of benefits anddrawbacks of this technology for
PRA. We make a preliminary analysis of possible application of LLM
inProbabilistic Risk Assessment (PRA) modeling context referring to the ongoing
experience in softwareengineering field. We explore potential application
scenarios and the necessary conditions for controlledLLM usage in PRA modeling
(whether static or dynamic). Additionally, we consider the potential impact
ofthis technology on PRA modeling tools.
在生成式人工智能(AI)领域,大型语言模型(LLM)和生成式预训练变换器(GPT)的快速发展会对现代经济中的任务自动化产生重大影响。我们预计,PRA 领域将不可避免地受到这项技术的影响1。因此,本文的主要目标是让风险评估社区参与讨论该技术对 PRA 的利弊。我们参考软件工程领域的现有经验,初步分析了 LLM 在概率风险评估(PRA)建模中的可能应用。我们探讨了潜在的应用场景,以及在 PRA 建模(无论是静态还是动态)中使用受控LLM 的必要条件。此外,我们还考虑了该技术对 PRA 建模工具的潜在影响。