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Benchmarking LLM Compliance with China AI Generated Content Regulations
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Chenrui Cui, Hongye Fang, Lisha Song, Weichao Chen, Yue Zhu, Gang Xu

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ResearcharXiv cs.CL

Benchmarking LLM Compliance with China AI Generated Content Regulations

arXiv:2609.19989v1 Announce Type: new Abstract: The widespread adoption of LLMs has led to escalating content compliance risks. Prior works have contributed to addressing these risks in the English context, downplaying the complexity of Chinese language content. This paper follows China's current AI-Generated content compliance requirements and provides evaluation results on 20 notable LLMs, offering insight into China's regulatory landscape. We design a novel framework to assess the compliance and refusal rates with 2303 questions spanning six distinct dimensions, including 203 self-constructed constitutional questions. The framework employs several judges to generate verdicts independently based on their hierarchical alignment memory. Our findings show that international models also exhibit high levels of compliance despite the use of standard Chinese questions, and the main differences may stem from dimensions closely related to ideological alignment. We establish a regulatory benchmark that enables the global AI community to evaluate both Chinese and non-Chinese LLMs under a unified set of legally grounded compliance requirements.

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This story was published by arXiv cs.CL and written by Chenrui Cui, Hongye Fang, Lisha Song, Weichao Chen, Yue Zhu, Gang Xu. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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