With the recent proliferation of large language models (LLMs), enterprises have been able to rapidly develop proof-of-concepts and prototypes. As a result, there is a growing need to implement robust guardrails that monitor, quantize and control an LLM's behavior, ensuring that the use is reliable, safe, accurate and also aligned with the users' expectations. Previous approaches for filtering out inappropriate user prompts or system outputs, such as LlamaGuard and OpenAI's MOD API, have achieved significant success by fine-tuning existing LLMs. However, using fine-tuned LLMs as guardrails introduces increased latency and higher maintenance costs, which may not be practical or scalable for cost-efficient deployments. We take a different approach, focusing on fine-tuning a lightweight architecture: Sentence-BERT. This method reduces the model size from LlamaGuard's 7 billion parameters to approximately 67 million, while maintaining comparable performance on the AEGIS safety benchmark.

本研究解决了大语言模型（LLMs）在企业应用中对安全监控和控制的需求缺乏高效解决方案的难题。论文提出了一种新的方法，采用微调轻量级架构Sentence-BERT，极大降低了模型规模，同时在AEGIS安全基准测试中保持了可比性能。研究结果表明，这种方法不仅减少了延迟和维护成本，还为安全部署提供了可扩展的解决方案。

轻量级安全保护栏：使用微调的BERT嵌入