A KNOWLEDGE GRAPH IS ALL YOU NEED

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.