Research on Public Security Professional Small Sample Knowledge Extraction Method Based on Large Language Model

The rapid development of informatization and digitalization in public security business has generated a large amount of law enforcement case data in public security work. However, due to various types of text and large amount of information, front-line police officers often face problems such as low reading efficiency and difficulty in aggregating information in the process of reading case files. In order to further utilize the law enforcement case text, it is necessary to conduct intelligent analysis and knowledge extraction. However, due to the professionalism, data sensitivity, confidentiality of public security professional law enforcement case text, as well as the requirements of public security data going out of the network, only a small number of learning training samples can be obtained, and the traditional deep learning model has unsatisfactory extraction effect. Therefore, this paper proposes to build a large language model in vertical fields with fewer resources and data, and realize the adaptation of the model to the public security profession. The model uses knowledge editing technology MEMIT (mess-editing memory in a transformer), low-resource fine-tuning technology LoRA (low-rank adaptation), and prompt templates to improve the model??s understanding of public security knowledge such as police terminology and common sense. Moreover, in order to further improve the knowledge extraction effect of the model, a small sample law enforcement case text data extraction process is designed to better integrate the professional knowledge related to the case in the model. Experimental results show that the accuracy of the public security professional vertical field large language model integrated with the extraction process in various knowledge extraction tasks is significantly improved compared with the traditional methods, which helps front-line police officers quickly, objectively and accurately analyze law enforcement case text, dig out potential case information, and support the intelligent development of public security work.