Fence: Specialized SLM Guardrails for LLM Applications
Quick Answer
This paper proposes using Small Language Models (SLMs) trained on synthetic data as specialized guardrails for large language models (LLMs) to enhance safety measures against issues like hallucination and topic drift.
Quick Take
A novel synthetic data generation method inspired by GANs shows that SLM guardrails outperform traditional prompt-based guardrails in performance.
Key Points
- SLMs trained on synthetic data serve as effective guardrails for LLM applications.
- The proposed method generates high-quality synthetic data using GAN-inspired techniques.
- SLM guardrails demonstrate performance improvements over prompt-based LLM guardrails.
- Challenges include data scarcity and high annotation costs for specialized guardrails.
- Application-specific guardrails address complex issues like hallucination and topic drift.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.18268 [cs.AI] |
| (or arXiv:2607.18268v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18268 arXiv-issued DOI via DataCite |
Submission history
From: Kumud Lakara [view email]
[v1]
Fri, 22 May 2026 16:05:50 UTC (44 KB)
— Originally published at arxiv.org
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