# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
###### Abstract
Taking the personnel organizations involved in police incidents as an example, relying solely on traditional models to extract keywords such as \"teaching, giving lessons\" does not fulfill the demands of final analysis, necessitating further analysis and summary by police personnel; the models currently used for extracting police incident elements lack the capability to directly extract semantic understanding-based organizational labels such as \"teacher\". From the perspective of police incident data, the amount of data generated each year is vast, but there is a scarcity of labeled data with tag information; moreover, due to the diversity and complexity of the content types contained in police incident data, the quality of the data is low and the dimensions are high. Building a training set for the extraction of key elements from police incidents requires domain-specific knowledge and operational experience from public security professionals, while also considering data privacy and security. In the context of the aforementioned public security operations, how to improve the effectiveness of key element extraction from police incident information is the focus of this paper. With the development of emerging technologies, especially large language models (LLMs), which demonstrate exceptional capabilities and powerful generalization in natural language processing tasks, these models have become a significant force driving research and application trends. How to efficiently extract key information from vast and complex data using these technologies has become a hot and challenging topic in the field of public security police incident information processing [12]. Currently, there are many studies on applying large language models to information extraction, achieving results superior to traditional models. Additionally, numerous studies have focused on building specialized domain-specific large language models for particular fields, helping to address unique issues within those domains [13; 14; 15; 16; 17; 18; 19; 20; 21]. This paper first explores the application of large language model (LLM) technology in the field of police incident element entity extraction. Given the sensitivity of police incident data and the limitations of current open-source models, we propose a method suitable for the public security sector in China. Initially, the key elements to be extracted from police incidents are categorized and defined, with the key elements involved in police incidents divided into four categories: key actions, key items, organizations, and locations,based on the relevant literature and expert experience, and each category is defined and explained in detail. Subsequently, we enhance the application of LLM technology in police incident element entity extraction from three main directions: prompt engineering, data engineering, and the selection and fine-tuning of open-source base models, while designing or improving methods specific to the public security domain. In the prompt engineering part, a prompt enhancement method is used to select optimal prompts to better leverage the capabilities of large language models; in the data engineering part, a method is proposed that utilizes a powerful proprietary large language model to enhance the capabilities of local open-source large language models, specifically a zero-sample police incident data augmentation method based on ChatGPT, which expands data without revealing privacy to enhance the large language model's effectiveness in extracting element entities. After extracting the elements, a vectorization approach with ChatGPT is proposed to align the extracted element entities. Ultimately, we designed and implemented a method for extracting element entities from police incidents based on open-source large language models, specifically for the public security domain.