Summary of Chinese Safetyqa: a Safety Short-form Factuality Benchmark For Large Language Models, by Yingshui Tan et al.
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
by Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiangyong Zhu, Bo Zheng, Kaifu Zhang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces the Chinese SafetyQA benchmark to evaluate the factuality ability of Large Language Models (LLMs) in answering short questions related to safety knowledge. The authors demonstrate that the accuracy, comprehensiveness, and clarity of LLMs’ understanding of safety knowledge are crucial for deploying them safely and compliantly. The proposed benchmark is designed to assess existing LLMs’ factuality abilities and analyze their relationships with other capabilities, such as robustness against attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well large language models understand safety information. It makes sure these models are accurate and clear in answering short questions about law, policy, and ethics. The authors show that this understanding is important for using the models safely and following rules. They make a special set of questions and answers to test how well existing models do on this task. |
