Summary of Cpsdbench: a Large Language Model Evaluation Benchmark and Baseline For Chinese Public Security Domain, by Xin Tong et al.
CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain
by Xin Tong, Bo Jin, Zhi Lin, Binjun Wang, Ting Yu, Qiang Cheng
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have shown impressive results across various application domains. This study aims to develop a specialized evaluation benchmark, called CPSDbench, tailored to the Chinese public security domain. CPSDbench integrates datasets from real-world scenarios, allowing for a comprehensive assessment of LLMs across four key dimensions: text classification, information extraction, question answering, and text generation. The authors introduce innovative evaluation metrics designed to quantify the efficacy of LLMs in addressing public security tasks. This research not only provides insights into the performance strengths and limitations of existing models but also serves as a reference for developing more accurate and customized LLM models for public security applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many things, like understand text and answer questions. But how well do they work when it comes to important tasks like keeping people safe? To find out, researchers created a special test called CPSDbench that uses real-world data from Chinese public security scenarios. The test looks at four different ways LLMs might be used: classifying texts, finding important information, answering questions, and generating new text. The researchers also came up with new ways to measure how well the models do these tasks. By studying this, we can learn more about what LLMs are good at and not so good at when it comes to public security. |
Keywords
» Artificial intelligence » Question answering » Text classification » Text generation
