Efficient Safety Alignment of Language Models via Latent Personality Traits
For developers of large language models, LPA provides a highly efficient and robust safety alignment method that eliminates the need for harmful training data and maintains utility.
Latent Personality Alignment (LPA) achieves near-zero attack success rates on HarmBench across direct requests and five jailbreak methods, using only 66 harm-agnostic statements and completing training in minutes on a single GPU, with 75x fewer examples than standard LAT and no loss of performance on standard benchmarks.
Current safety methods for large language models are known to be vulnerable to adversarial attacks, motivating research into robust alternatives. Latent Adversarial Training (LAT) is among the most effective defenses, but can degrade utility and requires training on large datasets of harmful prompts. We introduce Latent Personality Alignment (LPA), which replaces explicit harm refusal with adversarial training on just 66 harm-agnostic statements drawn from psychometric personality literature. We hypothesize that personality-anchored representations share latent structure with harm avoidance, so adversarially stabilizing them implicitly constrains the subspace exploited by jailbreak attacks. LPA achieves near-zero attack success rates on HarmBench across direct requests and five jailbreak methods, despite never seeing harmful content during training and no loss of performance on standard benchmarks. Moreover, the training process is lightweight; the entire procedure completes in minutes on a single GPU and uses 75x fewer examples than standard LAT. Extensive ablations demonstrate the robustness, efficiency, and generalization of our method.