CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment
arXiv:2606.15396v1 Announce Type: new Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety guardrails excel in English or multilingual settings, they lack adaptation to Chinese-specific regulatory policies, cultural context and linguistic nuances, failing to support fine-grained risk classification for diverse deployment needs. In this paper, we introduce a 5-macro, 31-micro category fine-grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guar
The rapid deployment and increasing sophistication of large language models globally necessitate advanced safety guardrails, especially as these models are adopted in diverse cultural and regulatory contexts beyond their initial Western development.
The development of fine-grained, culturally specific safety guardrails for Chinese LLMs highlights a growing divergence in AI ethics and regulation, impacting market access and technology development for global AI players.
Previously universal or Western-centric AI safety mechanisms are now being challenged by nuanced, region-specific requirements, leading to fragmented development and deployment of LLMs.
- · Chinese AI developers
- · Chinese tech regulators
- · Localized AI service providers
- · Global LLM developers without localized safety
- · Companies seeking unified AI deployment strategies
- · Unregulated content platforms
Chinese LLMs will gain a competitive advantage in mainland China due to better compliance and cultural alignment.
Other nations or blocs might develop their own region-specific AI safety taxonomies and guardrails, leading to greater AI fragmentation.
This could accelerate the balkanization of AI development, with distinct national or regional AI ecosystems emerging, each optimized for local regulatory and cultural norms.
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Read at arXiv cs.CL