Most prior safety research of large language models (LLMs) has focused on
enhancing the alignment of LLMs to better suit the safety requirements of
humans. However, internalizing such safeguard features into larger models
brought challenges of higher training cost and unintended degradation of
helpfulness. To overcome such challenges, a modular approach employing a
smaller LLM to detect harmful user queries is regarded as a convenient solution
in designing LLM-based system with safety requirements.
In this paper, we leverage a smaller LLM for both harmful query detection and
safeguard response generation. We introduce our safety requirements and the
taxonomy of harmfulness categories, and then propose a multi-task learning
mechanism fusing the two tasks into a single model. We demonstrate the
effectiveness of our approach, providing on par or surpassing harmful query
detection and safeguard response performance compared to the publicly available
LLMs.

利用较小的大语言模型实现有害查询检测和安全响应，通过多任务学习机制融合两个任务到一个模型里，效果在公开的大语言模型上表现相当或超过有害查询检测和安全响应的性能。