Exploring the Application of Retrieval-Augmented Generation Technology in Defense Technology Intelligence

Large language models (LLMs) are impeded by issues such as spurious generation, knowledge obsolescence, and domain expertise deficiency, which constrain their efficacy in defense technology intelligence applications. Mitigating these challenges is imperative for enhancing the utility of these models in this domain. Grounded in the core requirements of defense technology intelligence, this paper provides an incisive analysis of the significance of Retrieval-Augmented Generation (RAG) in boosting the performance and applicability of LLMs. The paper explores the primary scenarios and deployment paradigms for RA G in defense technology intelligence. Additionally, it synthesizes the technical obstacles encountered during implementation and elucidates the countermeasures devised to overcome them. Our research demonstrates that RAG technology can significantly enhance the efficiency, precision, relevance, and timeliness of intelligence gathering, enabling LLMs to better meet the demands of defense technology intelligence. These findings highlight the promising potential of RAG in augmenting the capabilities of language models for critical defense applications, paving the way for future advancements in this emerging field.