Keeping AI Safe While It Learns

Standard fine-tuning of large language models rapidly erodes previously established safety alignments, with this degradation accelerating when exposed to maliciously crafted user data, while adapted continual learning strategies demonstrably preserve those safety constraints even under adversarial conditions.

New research shows how to prevent large language models from losing their safety guardrails as they are continuously updated with new information.