| Title: |
AI-Based DPCAP FAR/DFARS Change Support Tool ; Annual Acquisition Research Symposium Proceedings & Presentations |
| Authors: |
Ramirez-Marquez, Jose; Gorman, Joshua; Akram, Amer; Buettner, Douglas J.; Mayer, Brian B.; Butler, Patrick; Ramakrishnan, Naren; Freedman, Bradley |
| Publication Year: |
2025 |
| Collection: |
VTechWorks (VirginiaTech) |
| Description: |
The Department of Defenses 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. ; Published version ; Yes, full paper (Peer reviewed?) |
| Document Type: |
conference object |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://hdl.handle.net/10919/141681 |
| Availability: |
https://hdl.handle.net/10919/141681 |
| Rights: |
Public Domain (U.S.) ; http://creativecommons.org/publicdomain/mark/1.0/ |
| Accession Number: |
edsbas.9E113CD1 |
| Database: |
BASE |