用核电教材训练小模型，实现数据安全的专用语言系统。
Towards Secure and Private Language Models for Nuclear Power Plants
- 基于小型Transformer架构，在单块显卡上训练专用核电模型。
- 虽语料有限，但已能识别核领域专有词汇。
- 适合对数据隐私要求高的工业场景，如核电站智能运维。
本文提出一种面向核电领域的专用大语言模型，基于公开可获取的《基础坎杜教材》构建。采用紧凑的Transformer架构，仅用单个GPU完成训练，以保护核电运营中的敏感数据。尽管数据集相对较小，模型已展现出捕捉核电专业术语的能力，但生成文本在句法连贯性方面仍显不足。通过聚焦核能内容，该方法验证了符合严格网络安全与数据保密标准的本地化大模型解决方案的可行性。早期文本生成成果表明其在特定任务中的实用价值，同时也揭示出需扩充语料、优化预处理及指令微调以提升领域准确性。未来工作包括扩展覆盖核电多子领域的数据集，改进分词策略以减少噪声，并系统评估模型在真实核电场景中的适用性。
原文摘要 · Abstract (English)
This paper introduces a domain-specific Large Language Model for nuclear applications, built from the publicly accessible Essential CANDU textbook. Drawing on a compact Transformer-based architecture, the model is trained on a single GPU to protect the sensitive data inherent in nuclear operations. Despite relying on a relatively small dataset, it shows encouraging signs of capturing specialized nuclear vocabulary, though the generated text sometimes lacks syntactic coherence. By focusing exclusively on nuclear content, this approach demonstrates the feasibility of in-house LLM solutions that align with rigorous cybersecurity and data confidentiality standards. Early successes in text generation underscore the model's utility for specialized tasks, while also revealing the need for richer corpora, more sophisticated preprocessing, and instruction fine-tuning to enhance domain accuracy. Future directions include extending the dataset to cover diverse nuclear subtopics, refining tokenization to reduce noise, and systematically evaluating the model's readiness for real-world applications in nuclear domain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。