# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
###### Abstract  
With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10-50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the \"organization\" element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome. large language models; police reports; information extraction; data enhancement; fine-tuning +
Footnote †: journal: Medical  
analyze a large amount of police data on theft cases and extract key elements (such as behaviors, items, locations, and organizations) from these cases, we can uncover some potential patterns. For example, in specific areas (such as Xicheng District, Beijing), there is a higher frequency of theft behaviors (such as lock-picking) and the theft of specific items (such as valuable electronic devices). Additionally, certain organizations (such as criminal gangs) are more active in these areas. By identifying these patterns, law enforcement agencies can deploy police forces more effectively in high-risk areas, enhancing patrol and surveillance to prevent crime. These patterns also provide an important basis for crime prediction, helping law enforcement agencies take preventive measures before incidents occur. For example, by predicting the crime risk in a specific area over a future period, the police can pre-arrange their forces, thereby improving the efficiency of police resource utilization. However, the heterogeneous nature of police incident data from multiple sources presents traditional manual analysis methods with challenges such as time-consuming processes, low efficiency, and difficulty in associative analysis [3]. As the volume of police incidents continues to increase, there is a lag in advancing data utilization capabilities; current police incident analysis often relies solely on experience accumulation or apprenticeship transmission, depending on manual analysis and traditional technological means to extract entities from police incidents, such as using dictionaries or models like LSTM and Bert to extract key elements [4; 5; 6; 7; 8; 9; 10; 11]. While these traditional methods have achieved certain results, the models used lack upgradability and generalizability, are numerous and varied, and do not meet the requirements for detailed analysis of police incidents; detailed analysis of police incidents requires certain semantic understanding capabilities.