Specialized SLM Guardrails Enhance LLM Application Safety

Real-world applications leveraging large language models (LLMs) require more sophisticated safety mechanisms than standard content filters. While general filters handle issues like toxicity, application-specific concerns such as hallucination, topic drift, and behavioral deviations are harder to define and model, especially given data scarcity and annotation costs. This paper introduces a new approach using Small Language Models (SLMs) as specialized guardrails. These SLMs are trained on synthetically generated data, utilizing a novel method inspired by Generative Adversarial Networks (GANs) to produce high-quality samples. This allows the SLMs to learn and enforce use-case-specific safety protocols. Experimental results indicate that SLM guardrails, when trained with this high-quality synthetic data, outperform prompt-based LLM guardrails in performance.