Sparse autoencoders plus greedy filtering and factorization-machine interaction modeling identify minimal sets of features in Gemma-2-2B-IT and LLaMA-3.1-8B-IT whose ablation produces jailbreaks by flipping refusal to compliance.
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MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.
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Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal
Sparse autoencoders plus greedy filtering and factorization-machine interaction modeling identify minimal sets of features in Gemma-2-2B-IT and LLaMA-3.1-8B-IT whose ablation produces jailbreaks by flipping refusal to compliance.
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MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender
MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.