DLR-Lock locks open-weight LLMs against unauthorized fine-tuning by swapping MLPs for deep low-rank residual networks that inflate backprop memory and complicate optimization, yet preserve original capabilities via module-wise distillation.
On the societal impact of open foundation models
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AI model evaluations for biological capabilities should prioritize high-consequence risks like pandemics, informed by life sciences dual-use experience, and occur prior to deployment to enable biosafety measures.
citing papers explorer
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Locking Pretrained Weights via Deep Low-Rank Residual Distillation
DLR-Lock locks open-weight LLMs against unauthorized fine-tuning by swapping MLPs for deep low-rank residual networks that inflate backprop memory and complicate optimization, yet preserve original capabilities via module-wise distillation.
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Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models
AI model evaluations for biological capabilities should prioritize high-consequence risks like pandemics, informed by life sciences dual-use experience, and occur prior to deployment to enable biosafety measures.