No more waiting for real data - train guardrails with synthetic prompts today
This paper introduces a methodology to develop guardrails for LLMs without needing real-world data. The approach uses synthetic data generation and classifier models to detect off-topic prompts, achieving 99% precision in preventing misuse while generalizing to detect jailbreaks and harmful content.
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https://arxiv.org/abs/2411.12946
🚨 Original Problem:
LLMs often respond to off-topic prompts outside their intended scope, creating compliance risks. Current guardrail solutions need real-world data or extensive examples, which aren't available pre-deployment.
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🔧 Solution in this Paper:
→ The paper proposes generating synthetic datasets using LLMs to create diverse system and user prompt pairs.
→ A bi-encoder classifier architecture processes system and user prompts separately with cross-attention layers.
→ The classifier determines if user prompts are relevant to the system prompt's defined scope.
→ The methodology frames detection as a relevance classification task, allowing it to catch jailbreaks and harmful content.
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💡 Key Insights:
→ Pre-deployment guardrails can be built without real-world data
→ Synthetic data generation through LLMs provides sufficient training examples
→ Framing detection as relevance classification improves generalization
→ Well-calibrated probability scores enable flexible deployment thresholds
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📊 Results:
→ 99% precision and recall on synthetic dataset
→ 92% ROC-AUC score on JailbreakBench
→ Processes 2,216 prompt pairs per minute
→ Generalizes effectively to harmful content detection with 86-99% recall
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