Democratizing Data: An Intelligent Querying System for Marine Corps Data
Name
Johnson_Final_Paper.pdf
Description
Technical Paper
Size
145.5 KB
Format
Adobe PDF
Checksum (MD5)
f86801500a85b201519332203f456965
Author(s)
Johnson, Lane
Nam, Kevin
Date Issued
September 10, 2025
Abstract
This research presents the development and implementation
of a text-to-Structured Query Language (SQL)
system tailored for Marine Corps logistics, capitalizing upon
the proven capabilities of Large Language Models (LLMs). By
fine-tuning an open-source LLM on a curated Global Combat
Support System - Marine Corps supply and maintenance dataset,
we demonstrate how non-technical users can intuitively interact
with Marine Corps data through natural language queries,
enhancing data accessibility and operational decision-making.
Our approach assumes a resource-constrained environment,
demonstrating that fine-tuning and deploying the model on a
single NVIDIA A100 graphics processing unit (GPU) is not
only feasible, but also highlights the potential for local or edgebased
artificial intelligence (AI) solutions. We further identify the
critical importance of high-quality, representative datasets and
propose a hybrid approach combining prompt engineering with
fine-tuning to improve performance. Our findings culminate in
concrete recommendations for the Marine Corps regarding data
governance, AI integration, and workforce development.
Subjects
Artificial Intelligence
Large Language Models
Text-to-SQL
Fine-tuning
Natural Language Processing
Data Governance
Logistics
LLSC
Military
MIT Department
Lincoln Laboratory
Persistent DSpace Link
