Lightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training

TL;DR

This paper presents a lightweight safety guardrail framework for language models that uses synthetic data and RL-guided adversarial training to improve content moderation, outperforming larger models in efficiency and robustness.

Contribution

The paper introduces a novel approach combining synthetic data generation and adversarial training with reinforcement learning to enhance small language models for safety tasks.

Findings

Small models achieve comparable or better safety performance than larger models.

Synthetic data and adversarial training improve robustness against harmful content.

Framework reduces computational costs while maintaining high effectiveness.

Abstract

We introduce a lightweight yet highly effective safety guardrail framework for language models, demonstrating that small-scale language models can achieve, and even surpass, the performance of larger counterparts in content moderation tasks. This is accomplished through high-fidelity synthetic data generation and adversarial training. The synthetic data generation process begins with human-curated seed data, which undergoes query augmentation and paraphrasing to create diverse and contextually rich examples. This augmented data is then subjected to multiple rounds of curation, ensuring high fidelity and relevance. Inspired by recent advances in the Generative Adversarial Network (GAN) architecture, our adversarial training employs reinforcement learning to guide a generator that produces challenging synthetic examples. These examples are used to fine-tune the safety classifier, enhancing its ability to detect and mitigate harmful content. Additionally, we incorporate strategies from recent research on efficient LLM training, leveraging the capabilities of smaller models to improve the performance of larger generative models. With iterative adversarial training and the generation of diverse, high-quality synthetic data, our framework enables small language models (SLMs) to serve as robust safety guardrails. This approach not only reduces computational overhead but also enhances resilience against adversarial attacks, offering a scalable and efficient solution for content moderation in AI systems.