Abstract
As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in static evaluation settings, face two major limitations in real-world applications: (1) they typically output only binary "safe/unsafe" labels, which can be interpreted inconsistently across diverse safety policies, rendering them incapable of accommodating varying safety tolerances across domains; and (2) they require complete model outputs before performing safety checks, making them fundamentally incompatible with streaming LLM inference, thereby preventing timely intervention during generation and increasing exposure to harmful partial outputs. To address these challenges, we present Qwen3Guard, a series of multilingual safety guardrail models with two specialized variants: Generative Qwen3Guard, which casts safety classification as an instruction-following task to enable fine-grained tri-class judgments (safe, controversial, unsafe); and Stream Qwen3Guard, which introduces a token-level classification head for real-time safety monitoring during incremental text generation. Both variants are available in three sizes (0.6B, 4B, and 8B parameters) and support up to 119 languages and dialects, providing comprehensive, scalable, and low-latency safety moderation for global LLM deployments. Evaluated across English, Chinese, and multilingual benchmarks, Qwen3Guard achieves state-of-the-art performance in both prompt and response safety classification. All models are released under the Apache 2.0 license for public use.
Abstract (translated)
随着大型语言模型（LLM）的能力和使用范围不断扩大，确保其输出的安全性变得越来越重要。现有的防护模型虽然在静态评估环境中很有用，但在实际应用中面临两个主要限制：(1) 它们通常仅提供二元的“安全/不安全”标签，在面对多样的安全政策时可能导致解释上的不一致，无法适应不同领域中的不同安全容忍度；(2) 它们需要在完成模型输出之后才能进行安全性检查，这使得它们与流式LLM推理本质上是不兼容的，因此无法及时干预生成过程，并增加了暴露于有害部分输出的风险。为了解决这些问题，我们提出了Qwen3Guard，一系列多语言安全防护模型，包含两种专门化的变体：Generative Qwen3Guard将安全性分类作为一种指令跟随任务来实现细粒度三元判断（安全、有争议、不安全）；Stream Qwen3Guard则引入了一个令牌级别的分类头部以实现在增量文本生成过程中的实时安全监测。这两种变体分别提供0.6B、4B和8B参数大小的模型，并支持多达119种语言和方言，为全球LLM部署提供了全面、可扩展且低延迟的安全管理方案。Qwen3Guard在英语、中文以及多语言基准测试中，在提示和响应安全性分类方面均达到了最先进的性能水平。所有模型均以Apache 2.0许可证发布供公众使用。
URL
https://arxiv.org/abs/2510.14276
