Experimental Analysis of Large Language Models in Crime Classification and Prediction

Increasing crime rates and evolving challenges in law enforcement have raised the need for innovative solutions, leading to the emergence of smart policing. This paradigm shift incorporates artificial intelligence (AI) with a specific focus on machine learning (ML) as a pivotal tool for data analysis, pattern recognition, and proactive crime forecasting. Large-language models (LLMs) as a subset of generative AI have been used in different domains, such as financial, medical, legal, and agricultural applications. However, the abilities and possibilities of adopting LLMs for smart policing applications such as crime classification remain unexplored. This paper explores the transformative potential of BART, GPT-3, and GPT-4, three state-of-the-art LLMs, in the domain of crime analysis and predictive policing. Utilizing diverse methods such as zero-shot prompting, few-shot prompting, and fine-tuning, this paper evaluates the performance of these models on state-of-the-art datasets from two major cities: San Francisco and Los Angeles. The goal is to demonstrate the adaptability of LLMs and their capacity to revolutionize conventional crime analysis practices. The paper also provide a comparative analysis of the aforementioned methods on the GPT series model and BART, in addition to ML techniques, showing that GPT models are more suitable for crime classification in most of our experimental scenarios.