Philipp Normann

Towards Explaining Classification Models in Security with Sparse Autoencoders

Sparse Autoencoders (SAEs) offer a promising unsupervised interpretability approach for extracting human-interpretable concepts from large language models. Yet, their use in the security domain remains underexplored. Security-related classification tasks typically rely on smaller models than those commonly studied with...