AGENTIC-SYSTEMSMay 13, 20261 min read10 views

Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms

Researchers propose Latent Personality Alignment (LPA), a sample-efficient defense that achieves LLM robustness by training on abstract personality traits rather than specific harmful behaviors, requiring fewer than 100 examples.

Computer Science > Artificial Intelligence

Title:Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms

Abstract: Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to hundreds of thousands of examples), yet remain vulnerable to novel attack vectors and distributional shifts. We propose Latent Personality Alignment (LPA), a sample-efficient defense that achieves robustness by training models on abstract personality traits rather than specific harmful behaviors. Using fewer than 100 trait statements and latent adversarial training, LPA achieves comparable attack success rates to methods trained on 150k+ examples, while maintaining superior utility. Critically, LPA generalizes better to unseen attack distributions, reducing misclassification rates by 2.6x compared to baseline across six harm benchmarks -- without ever seeing harmful examples during training. Our results demonstrate that personality-based alignment offers a principled approach to building robust defenses with minimal cost.

Source: arXiv cs.AI Recent

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