A Framework for LLM-Assisted Smart Policing System

Abstract

In the face of rapidly increasing crime rates, the evolving complexity of crime data processing, and public safety challenges, the need for more advanced policing solutions has increased leading to the emergence of smart policing systems and predictive policing techniques. This urgency and shift toward smart policing incorporates artificial intelligence (AI), with a specific focus on machine learning (ML) as an essential tool for data analysis, pattern recognition, and proactive crime forecasting. Among these, the flexibility and power of AI techniques including large language models (LLMs), as a subset of generative AI, have increased the interest in applying them in real-world applications, such as financial, medical, legal, and agricultural applications. However, the abilities and possibilities of adopting LLMs in applications including crime prediction remain unexplored. This paper focuses on bridging this gap by developing a framework based on the transformative potential of BART, GPT-3, and GPT-4, three state-of-the-art LLMs, in the domain of smart policing, specifically, crime prediction. As a prototype, diverse methods such as zero-shot prompting, few-shot prompting, and fine-tuning are used to comprehensively assess the performance of these models in crime prediction based on state-of-the-art datasets from two major cities: San Francisco and Los Angeles. The main objective is to illuminate the adaptability of LLMs and their capacity to revolutionize crime analysis practices. Additionally, a comparative analysis of the aforementioned methods on the GPT series model and BART with ML techniques is provided which shows that the GPT models are more suitable than the traditional ML models for crime classification in most experimental scenarios.

Keywords

• Crime prediction
• fine-tuning
• few-shot prompting
• large language models
• LLM
• zero-shot prompting