# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
## 6 Conclusions  
This study applies large language model technology to the automated extraction of police incident elements, significantly improving efficiency and saving labor compared to manual processing methods. It overcomes the inefficiencies and lack of semantic understanding in traditional information extraction methods when dealing with large-scale, multi-source heterogeneous police incident data. By combining fine-tuning with base model selection, data engineering, and prompt engineering, different open-source base models were tested. The proposed ChatGPT-based zero-shot data augmentation method, along with prompt design and selection methods, resulted in certain improvements in the effectiveness of police incident element extraction. By choosing the optimal strategy for each part, the most effective large language model for police incident element extraction was obtained. Finally, entity alignment of the extracted police incident elements using ChatGPT was proposed. Experimental results show that our method has significant improvements in police incident element extraction compared to the current state-of-the-art (SOTA) general information extraction models.  
In conducting this research, we encountered several major challenges. The primary challenge arose from restrictions related to data privacy and sensitivity. Given that police incident data in China are classified as sensitive information, we had to develop and train our models while strictly adhering to protocols for protecting personal information and sensitive data. This is also why we opted for open-source models for local deployment. In the realm of data engineering, we faced potential model bias issues caused by non-uniform data distribution. To mitigate this, we prioritized ensuring diversity and representativeness of police incidents and extracted elements. Despite these challenges, our research demonstrates the potential of applying large language models to police incident information extraction.  
However, this study has certain limitations: There are numerous open-source large language models available, but comprehensively testing all of them was beyond the scope of this research. Additionally, large language models are prone to generating \"hallucinations\", which can affect the reliability of outputs. Future work will focus on enhancing the adaptability and scalability of the model, developing its application to multilingual police incident data, improving multimodal capabilities and interactivity, optimizing interaction design, strengthening data security and compliance, and exploring cross-domain applications.  
Conceptualization, X.X.; methodology, X.X.; validation, X.X.; formal analysis, X.X.; data curation, X.X.; writing--original draft preparation, X.X.; writing--review and editing, P.C. and X.X.; visualization, X.X.; supervision, P.C.; project administration, P.C.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This research received Funding for Discipline Innovation and Talent Introduction Bases in Higher Education Institutions (B20087).  
Data Availability Statement: Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
We thank the Beijing Municipal Public Security Bureau for authorizing us to use their data for this research. We also thank anonymous reviewers for their comments on improving this manuscript.  
The authors declare no conflicts of interest.