Crime Incident Prediction Using LLM GPT and XLNET Algorithm

Abstract

The rise of Large Language Models (LLMs) has opened new frontiers in various domains, including law enforcement. This introduces a novel framework for a Smart Policing System enhanced by the latest advancements in LLM technology. Building on existing methodologies such as the proposed framework integrates GPT-2 and XLNet to improve the efficiency and accuracy of predictive policing, crime analysis, and decision-making processes. By leveraging the advanced capabilities of GPT-2 in understanding and generating human-like text and the contextual power of XLNet, our framework aims to offer enhanced analytical insights, real-time threat assessment, and more effective resource allocation. This system not only aims to optimize operational performance but also addresses ethical considerations and privacy concerns inherent in smart policing technologies. Our framework represents a significant step towards more intelligent, adaptive, and responsive law enforcement solutions in the modern age.