用大模型加速政府科研机构的文档分析，安全可控且只需少量示例。
Generative AI for FFRDCs
- 仅需少量示例，即可用大模型快速处理政策与科研文档
- 在国防法案和基金资助数据上实现高效分类与关键信息提取
- 支持本地部署，保障数据主权与审计可追溯性，适合敏感场景
联邦资助的研发中心（FFRDCs）面临大量文本工作，如政策文件和科学工程论文，手动分析效率低下。我们展示如何利用大语言模型，仅需少量输入输出示例，即可加速摘要生成、分类、信息抽取和意义理解。为适应敏感政府环境，采用OnPrem.LLM开源框架，实现生成式AI的安全灵活应用。在国防政策文件及科学文献语料（包括《国防授权法案》(NDAA) 和国家科学基金会(NSF) 资助项目）上的案例研究显示，该方法显著提升监督与战略分析能力，同时确保可审计性和数据主权。
原文摘要 · Abstract (English)
Federally funded research and development centers (FFRDCs) face text-heavy workloads, from policy documents to scientific and engineering papers, that are slow to analyze manually. We show how large language models can accelerate summarization, classification, extraction, and sense-making with only a few input-output examples. To enable use in sensitive government contexts, we apply OnPrem$.$LLM, an open-source framework for secure and flexible application of generative AI. Case studies on defense policy documents and scientific corpora, including the National Defense Authorization Act (NDAA) and National Science Foundation (NSF) Awards, demonstrate how this approach enhances oversight and strategic analysis while maintaining auditability and data sovereignty.
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