Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914546233376768 |
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| author | Le, Linh Williams-King, David Merzouk, Mohamed Amine Kamanda, Aton Oberman, Adam |
| author_facet | Le, Linh Williams-King, David Merzouk, Mohamed Amine Kamanda, Aton Oberman, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08496 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms Le, Linh Williams-King, David Merzouk, Mohamed Amine Kamanda, Aton Oberman, Adam Artificial Intelligence I.2.7 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. |
| title | Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms |
| topic | Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2605.08496 |