关键词: 大语言模型, 信息抽取, 指令微调, 公安业务, 命名实体识别

Abstract: With the rapid development of artificial intelligence technology, the strategy of “technology-driven policing” has become an important way to enhance the modernization level of public security work.  Under the background of technology-driven policing, public security organs are faced with the demand for processing a large amount of unstructured case text information, and the traditional manual processing method can no longer meet the current work requirements. Large language models, as an emerging artificial intelligence technology, have strong language understanding and generation capabilities, and can automatically extract key information elements from case texts, such as involved personnel, time, location, and case nature, providing strong support for case analysis, evidence collection, and decision support. This paper aims to study the method of case information element extraction based on instruction fine-tuning of large language models, in order to improve the efficiency and accuracy of public security organs in case information processing through advanced natural language processing technology, and further promote the informatization process of public security work. The research enhances the information extraction capability of large language models through techniques such as efficient fine-tuning via LoRA, instruction fine-tuning, data augmentation, and in-context learning. Experimental results show that this method achieves significant performance improvement on the self-built case text dataset, with both extraction accuracy and recall being better than traditional methods.

Key words: large language model, information extraction, instruction fine-tuning, police affairs, named entity recognition