Date of Award
6-2025
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Neil C. Ranly, PhD
Abstract
This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.
AFIT Designator
FY25-AFIT-ENS-MS-J-002
Recommended Citation
Washington, Aaron C., "Contract Quality Feature Extraction Using LLM" (2025). Theses and Dissertations. 8346.
https://scholar.afit.edu/etd/8346
Included in
Artificial Intelligence and Robotics Commons, Government Contracts Commons, Operational Research Commons
Comments
An embargo was observed for posting this thesis on AFIT Scholar.
Approved for public release, distribution unlimited. PA case number 88ABW-2025-0584