基于知识库的大型语言模型的辐射防护智能助手。

IF 1.5 4区 环境科学与生态学 Q3 BIOLOGY
Ankang Hu, Kaiwen Li, Zhen Wu, Hui Zhang, Rui Qiu, Junli Li
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引用次数: 0

摘要

辐射防护是支持核能和核技术利用的重要支柱。辐射防护体系是随着知识和经验的积累而建立起来的。然而,要全面细致地掌握相关知识和经验,对个人乃至一个委员会来说都是一项挑战。我们迫切需要一位在辐射防护方面具有丰富知识和经验的智能助手。在这项工作中,我们提出了一个基于知识库的大语言模型(LLM)的辐射防护智能助手。该助手可以根据权威出版物提供可靠的答案。该助手使用开源工具包和开源法学硕士开发,并对专业问题给出了令人满意的答案。用户可以通过基于web的用户界面(UI)获得有参考的可靠答案。该助手专为本地部署而设计,并利用私有数据集,从而解决与隐私和数据安全相关的问题。通过与带有网络搜索的法学硕士应用程序进行比较,评估了该助手的有效性。结果表明,与基于web搜索的系统相比,我们的方法在模型参数数量少得多的情况下,可以在辐射防护领域提供更精确和相关的响应。这项工作是在辐射防护领域建立智能助手的初步尝试,它显示了利用LLM提高辐射防护相关任务效率的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent assistant in radiation protection based on large language model with knowledge base.

Radiation protection is a critical pillar supporting the use of nuclear energy and nuclear technologies. The radiation protection system has been established with the accumulation of knowledge and experience. However, it is challenging for an individual or even a committee to master related knowledge and experience comprehensively and meticulously. An intelligent assistant that possesses extensive knowledge and experience in radiation protection is eagerly required. In this work, we propose an intelligent assistant in radiation protection based on a Large Language Model (LLM) with a knowledge base. The assistant can provide reliable answers with references from authoritative publications. The assistant was developed using open-source toolkits and open-source LLMs, and demonstrated satisfying answers to professional queries. Users can obtain reliable answers with references through the web-based user interface (UI). The assistant is designed for local deployment and utilizes private datasets, thereby addressing issues related to privacy and data security. The effectiveness of the assistant was evaluated by comparing it with LLM applications with web search. The results show that our method with a much smaller number of model parameters can deliver more precise and pertinent responses within the domain of radiation protection than web search-based systems. This work is a preliminary attempt to establish an intelligent assistant in the field of radiation protection, and it shows the potential for using LLM to increase efficiency in radiation protection-related tasks.

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来源期刊
CiteScore
4.00
自引率
5.90%
发文量
53
审稿时长
>36 weeks
期刊介绍: This journal is devoted to fundamental and applied issues in radiation research and biophysics. The topics may include: Biophysics of ionizing radiation: radiation physics and chemistry, radiation dosimetry, radiobiology, radioecology, biophysical foundations of medical applications of radiation, and radiation protection. Biological effects of radiation: experimental or theoretical work on molecular or cellular effects; relevance of biological effects for risk assessment; biological effects of medical applications of radiation; relevance of radiation for biosphere and in space; modelling of ecosystems; modelling of transport processes of substances in biotic systems. Risk assessment: epidemiological studies of cancer and non-cancer effects; quantification of risk including exposures to radiation and confounding factors Contributions to these topics may include theoretical-mathematical and experimental material, as well as description of new techniques relevant for the study of these issues. They can range from complex radiobiological phenomena to issues in health physics and environmental protection.
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