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
Source Excerpt
Real-world applications that use closed-source (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 guardrai
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for Agents
RAIL Guard introduces a closed-loop AI pipeline for large language models (LLMs) that evaluates outputs across eight dimensions and iteratively remediates failures, achieving 96.9% convergence compared to 49.1% for traditional block-and-retry methods. The system reduces unsafe agent executions by 33% without impacting task completion and is available as open-source SDKs.