Large Language Models (LLMs) have demonstrated significant potential and effectiveness across multiple application domains. To assess the performance of mainstream LLMs in public security tasks, this study aims to construct a specialized evaluation benchmark tailored to the Chinese public security domain--CPSDbench. CPSDbench integrates datasets related to public security collected from real-world scenarios, supporting a comprehensive assessment of LLMs across four key dimensions: text classification, information extraction, question answering, and text generation. Furthermore, this study introduces a set of innovative evaluation metrics designed to more precisely quantify the efficacy of LLMs in executing tasks related to public security. Through the in-depth analysis and evaluation conducted in this research, we not only enhance our understanding of the performance strengths and limitations of existing models in addressing public security issues but also provide references for the future development of more accurate and customized LLM models targeted at applications in this field.

翻译：大语言模型在多个应用领域已展现出显著潜力与有效性。为评估主流大语言模型在公共安全任务中的性能，本研究旨在构建一个面向中国公共安全领域的专门评估基准——CPSDbench。CPSDbench整合了从真实场景中收集的公共安全相关数据集，支持从文本分类、信息抽取、问答及文本生成四个关键维度对大语言模型进行综合评估。此外，本研究引入了一套创新性评估指标，旨在更精准地量化大语言模型在执行公共安全相关任务时的效能。通过本研究的深入分析与评估，不仅加深了我们对现有模型在应对公共安全问题时性能优势与局限性的理解，亦为未来开发更精准、更定制化的面向该领域应用的大语言模型提供了参考依据。