# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
## 2 Related Work
### Optimization Techniques for Large Language Models in Information Extraction
Studies have proven that the only data augmentation method that consistently improves model performance is generating data similar to the training data (using zero-shot or few-shot) rather than interpreting or modifying the current training set [38]. From the perspective of prompt engineering, zero-shot learning and prompt optimization techniques are continuously evolving. Zero-shot learning allows LLMs to perform untrained tasks, relying on the model's ability to generate coherent text [39; 40]. Prompt optimization includes various design methods, such as prompt optimization principles [41], prompt optimization using data gradients [42], adaptive prompt optimization EvoPrompt [43], multi-round dialogue alignment strategies [44], and PromptAgent [45]. These methods have achieved significant progress in improving model performance. Additionally, new techniques such as Chain of Thought (CoT) [46], Tree of Thoughts (ToT) [47], Chain-of-Symbol Prompting (CoS) [48], and Optimization by Prompting (OPRO) [49] have shown potential in enhancing reasoning capabilities and task adaptability [50]. Surveying current research, as society and technology continually evolve, the definition of police incident elements is in a state of ongoing development and refinement. Proposing and extracting elements that are appropriate for current conditions is essential. Currently, the extraction of key elements from police incidents is still confined to traditional methods. As analytical demands increase, the effectiveness of these extractions fails to meet requirements, and traditional methods cannot provide abstract summaries of extracted elements through semantic understanding. At the same time, large language model (LLM) technology is flourishing, achieving new heights across various traditional natural language processing tasks [21]; it has been successfully applied in general information extraction tasks, and domain-specific large language models are increasingly emerging in various fields. However, in the public security domain, there remains a gap in the practical application of large language models to police work and a lack of comprehensive processes for developing large language models tailored for sensitive areas. To address the issues mentioned above, this paper applies large language model (LLM) technology to the domain of police incident element entity extraction to enhance the extraction effectiveness of key incident elements. Recent research indicates that smaller models, through targeted training, can match the capabilities of large, parameter-rich models [29; 50], offering a technical solution that meets the specific requirements of the public security domain while reducing budgetary constraints. Tailoring to the characteristics of the public security field, we opt for local deployment of open-source large language models, and improve existing information extraction techniques. By testing and fine-tuning different base models and combining data engineering and prompt engineering techniques, we enhance the local model's capabilities for extracting key incident elements. Finally, using ChatGPT, we align the extracted elements. The approach proposed in this paper not only iterates on existing methods of police incident element extraction but also marks the first application of large language model technology in the public security incident domain.