  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.
