A KNOWLEDGE GRAPH IS ALL YOU NEED
Name
Smith_And_Brooks_Final_Paper.pdf
Description
Technical Paper
Size
761.97 KB
Format
Adobe PDF
Checksum (MD5)
37b6653fc74db82ade9a433707ecc093
Author(s)
Streilen, William
Brooks, Nicholas
Burill, Daniel
Smith, Corey
Date Issued
September 10, 2025
Abstract
The Department of the Air Force (DAF) faces
unique challenges in adopting Large Language Models
(LLMs). Commercially available models often lack the
domain-specific knowledge necessary to support airmen,
as this information is not inherently embedded. To maintain
a competitive edge, the integration of LLMs to
improve efficiency and decision making is a critical priority.
This presentation explores two innovative methodologies
designed to better integrate domain-specific knowledge
into language models and improve the discovery of
relevant information. The first is EntiGraph Continuous
Pretraining, which leverages continuous training to embed
specialized knowledge into language models. The second
is the GFM-RAG Graph RAG Framework, a novel approach
to knowledge retrieval and synthesis that enhances
model performance by improving multi-hop retrieval and
complex information connections.
Through both quantitative and qualitative evaluations, we
assess their impact on retrieval accuracy and response
relevance. Our findings demonstrate the potential of these
customized approaches to streamline information access,
improve decision making, and better support the operational
needs of the DAF.
Subjects
LLSC
Large Language Models
Retrieval-Augmented Generation
Artificial Intelligence
MIT Department
Lincoln Laboratory
Persistent DSpace Link
