A Staged Framework for LLM-powered Information Extraction in Government Contracts

Author

Jung H. Yae

Date of Award

3-2024

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Neil C. Ranly, PhD

Abstract

The manual extraction of meaningful insights and conversion of content into structured forms to enhance document processing require substantial resources and are susceptible to errors. Despite numerous applications of various Natural Language Processing (NLP) models to streamline the manual process, challenges persist due to domain-specific data constraints and the deficiency of annotated data. This study attempts to address these challenges by leveraging a Large Language Model (LLM) to analyze government contracts. Through rigorous evaluation, we demonstrate the LLM’s effectiveness in information extraction and mitigating hallucinations, achieving a 87.86% accuracy in metadata extraction.

AFIT Designator

AFIT-ENS-MS-24-M-103

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

Recommended Citation

Yae, Jung H., "A Staged Framework for LLM-powered Information Extraction in Government Contracts" (2024). Theses and Dissertations. 7734.
https://scholar.afit.edu/etd/7734