Data-Efficient LLM Safety Alignment Guided by Interpretable Features

Summary

Researchers propose Less is Safer (LIS), a 2.4K-sample dataset for LLM safety alignment, built by selecting prompts via Sparse Autoencoder (SAE) activations and by multi-agent adversarial synthesis of harmless prompts that still trigger unnecessary refusals. On four safety and five over-refusal benchmarks, LIS outperforms much larger datasets such as TRIDENT-Core (26K), TRIDENT-Edge (19K) and WildJailbreak (262K). Ablations show that safety data alone and over-refusal data alone each fall short of optimal results.