Please use this identifier to cite or link to this item: https://dair.nps.edu/handle/123456789/5415
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dc.contributor.authorJose Ramirez-Marquez-
dc.contributor.authorJoshua Gorman-
dc.contributor.authorAkram Amer-
dc.contributor.authorDouglas Buettner-
dc.contributor.authorBrian Maye-
dc.contributor.authorPatrick Butler-
dc.contributor.authorNaren Ramakrishnan-
dc.contributor.authorBradley Freedman-
dc.date.accessioned2025-05-12T21:24:57Z-
dc.date.available2025-05-12T21:24:57Z-
dc.date.issued2025-05-12-
dc.identifier.citationAPAen_US
dc.identifier.urihttps://dair.nps.edu/handle/123456789/5415-
dc.descriptionSYM Paperen_US
dc.description.abstract"The Department of Defense’s Defense Pricing, Contracting, and Acquisition Policy Contract Policy Directorate in the Office of the Assistant Secretary of Defense is responsible for periodic updates to the Federal Acquisition Regulation (FAR) and Defense FAR Supplement (DFARS) based on changes in the National Defense Authorization Act (NDAA), Small Business Administration rule changes, U.S. Department of Labor rule changes, or from executive orders. Reading through and assessing these documents for changes that require corresponding changes to acquisition regulations is labor-intensive. Further, when rule changes are proposed to the public for comments, reading and summarizing these public comments can range from straightforward to very labor-intensive. In this paper, we report our initial research results to greatly improve the efficiency of analyzing the NDAA language for required updates of the FAR and DFARS, and issuance of memoranda and guidance using artificial intelligence, including large language models and advanced natural language processing techniques to provide an improvement in staff efficiency for these laborious tasks."en_US
dc.description.sponsorshipAcquisition Research Programen_US
dc.language.isoen_USen_US
dc.publisherAcquisition Research Programen_US
dc.relation.ispartofseriesAcquisition Management;SYM-AM-25-363-
dc.subjectNatural Language Processingen_US
dc.subjectNLPen_US
dc.subjectNational Defense Authorization Acten_US
dc.subjectNDAAen_US
dc.subjectDefense FAR Supplementen_US
dc.subjectDFARSen_US
dc.titleAI-Based DPCAP FAR/DFARS Change Support Toolen_US
dc.typeTechnical Reporten_US
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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