Generative AI for FFRDCs
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 25 Sep 2025.
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References · 9
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023GPT-4 Technical Reportcited by 18,396
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
- 2022Efficient Few-Shot Learning Without Promptscited by 137
- 2025OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkitcited by 2
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