Can Benign Data Undermine AI Safety? This Paper from Princeton University Explores the Paradox of Machine Learning Fine-Tuning

Safety tuning is important for ensuring that advanced Large Language Models (LLMs) are aligned with human values and safe to deploy. Current LLMs, including those tuned for safety and alignment, are susceptible to jailbreaking. Existing guardrails are shown to be fragile. Even customizing models through fine-tuning with benign data, free of harmful content, could trigger […]

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