Fence: Specialized SLM Guardrails for LLM Applications
For developers of LLM applications needing cost-effective, specialized safety measures beyond basic content filters.
The paper proposes using Small Language Models (SLMs) trained on synthetic data as specialized guardrails for LLM applications, addressing issues like hallucination and topic drift. Their method shows performance gains over prompt-based LLM guardrails.
Real-world applications that use closed-source large language models (LLMs) need advanced safety measures that go beyond the basic content filters. Content moderation filters such as toxicity and bias have relatively standard definitions where as application specific guardrails like hallucination, topic drift and behaviour deviation are more difficult to model and can vary by use case. Additionally, data scarcity and annotation costs, make the process of creating and testing specialized guardrails challenging. In this work, we propose using Small Language Models (SLMs) trained on synthetic data as specialized guardrails for LLM applications. We introduce a novel synthetic data generation method inspired by the design of Generative Adversarial Networks (GANs) to generate high quality synthetic data samples which can be used to train SLMs to encode use case specific guardrail information and hence function as specialized guardrails. Our experiments demonstrate that SLM guardrails trained on high quality synthetic data show performance gains over prompt based LLM guardrails.