Earticle

초록

영어

Artificial intelligence (AI) is a significant tool in modern military operations in that it helps to analyze a large volume of strategic, tactical, and operational data. On the other hand, current large language models (LLMs) like GPT-4 or Falcon have difficulty resolving problems in defense-specific contexts because of issues related to security, data confidentiality, and the lack of explainability. This document presents MilGPT, a secure and explainable LLM structure that aims at solving military problems only. To the model, fine-tuned open-source architectures with domain-specific defense datasets are integrated to elevate intelligence synthesis, decision-making, and threat prediction. On the benchmark, performance evaluation tasks show that MilGPT accounts for a 27% increase in contextual accuracy, an 18% reduction in hallucination rate, and an 33% improvement in explainability as measured by gradient-based feature attribution. In the proposed framework, military intelligence systems are not only secured but also made adaptive and humaninterpretable, thus, setting up a basis for the coming generation of AI models capable of defense-grade tasks.

목차

Abstract
I. Introduction
II. Literature Review
III. Methodology
A. Data Curation and Preprocessing
B. Model Adaptation and Fine-Tuning
C. Explainability and Transparency Layer
D. Secure Model Deployment
E. Conceptual Architecture
IV. Results
A. Quantitative Evaluation
B. Mathematical Validation
C. Visualization of Model Performance
V. Discussion
VI. Conclusion
VII. References

저자

• Atif Ali [ Research Management Centre (RMC), Multimedia University, Cyberjaye 63100 Malaysia. ]
• Haroon Tariq Sheikh [ Iqra University Islamabad ]
• Ali Raza [ University of Gujrat, Pakistan ]
• Tariq Hanif [ UIIT PMAS Arid Agriculture University, Rawalpindi Pakistan ]
• Salman Ghani Virk [ Riphah International University, Islamabad, Pakistan ]
• Hina Riaz [ UIIT PMAS Arid Agriculture University, Rawalpindi, Pakistan ]

참고문헌