Enhancing Military Threat Analysis by Integrating LLM and RAG: A Review and Conceptual Framework

This research presents a conceptual framework for analyzing and predicting military threats using Large Language Models (LLMs) and the Retrieval-Augmented Generation (RAG) architecture. The proposed framework focuses on processing multimodal data, including textual, visual, audio, radar, electronic warfare (EW), and geospatial information, aiming to improve the accuracy of military data analysis and reduce processing time. The architecture consists of three main layers: data preprocessing, feature extraction, and final decision-making. In the preprocessing phase, data from various sources are collected and converted into standardized formats. In the feature extraction layer, the RAG architecture, leveraging vector databases, advanced search techniques such as Dense Retrieval and Retrieve-and-Rerank, and Knowledge Graphs, identifies and analyzes complex relationships among data. Large Language Models like GPT and BERT are employed to interpret context and generate precise textual responses. In the decision-making layer, the processed results are presented as actionable reports, which can be statistically analyzed and evaluated using standard metrics such as BLEU and ROUGE. This conceptual framework enables the integration of multimodal data and, by utilizing advanced techniques such as Few-Shot Prompting and Chain-of-Thought Reasoning, enhances the accuracy of threat prediction and identification. The findings indicate that combining RAG and LLM can significantly improve military information management, the analysis of complex relationships, and the delivery of precise strategic decision-making. This research provides recommendations for further development and sets the stage for practical implementation of the proposed framework in the future.

کلیدواژه ها:

Large Language Models (LLMs) ، RAG Architecture ، Multimodal Data Processing ، Knowledge Graphs ، Military Threat Analysis