探索大型语言模型在辐射紧急响应中的作用
Anirudh Chandra1, Abinash Chakraborty2
1Radiation Safety Systems Division, Bhabha Atomic Research Centre, Mumbai 400085, India.
概括
大型语言模型 (LLM) 通过理解复杂的查询来管理辐射紧急情况. 建议以专家指导,特定领域的LLM为核安全的可靠决策支持.
科学领域:
- 人工智能的人工智能
- 核安全问题 核安全问题
- 应急响应应对紧急情况的反应.
背景情况:
- 大型语言模型 (LLM) 已经通过先进的文本理解和生成改变了各种行业.
- 在核工业中LLM的应用,特别是用于辐射应急管理,仍然在很大程度上未被探索.
- 现有的人工智能能力在关键事件期间提供了增强通信和信息处理的潜力.
研究的目的:
- 探索LLMs在核工业内的辐射紧急情况管理中的潜力.
- 评估LLM在环境意识,自然语言交互和在辐射紧急环境中查询理解方面的能力.
- 确定LLM在辐射应急响应中的用户类型和特定用例.
主要方法:
- 模拟辐射应急人员与ChatGPT,一个著名的法学士之间的交互.
- 识别不同的用户角色及其相应的LLM应用场景.
- 分析LLM在理解和响应辐射紧急情况相关查询方面的表现.
主要成果:
- 初步结果表明,LLM具有上下文意识,并且可以处理与辐射紧急情况相关的各种查询.
- 确定了不同类型用户的特定用例,证明了LLM的适用性.
- 模拟提供了用户和LLMs之间的实际交互动态的见解.
结论:
- 在辐射应急管理中,LLM提供了一条有希望的途径来加强决策支持框架.
- 经过专家指导和特定领域的LLM,接受辐射安全协议和历史数据的培训,对于确保可靠性和减轻错误信息至关重要.
- 进一步发展和整合专门的LLM可以显著帮助实践者和决策者在核安全.
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