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cs.LG2025
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
Aaron Mueller, Andrew Lee, Shruti Joshi +3
A goal of interpretability is to recover disentangled representations of latent concepts (features) from the activations of neural networks. The quality of features is typically ev…
cs.LG2025
Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
Shruti Joshi, Andrea Dittadi, Sébastien Lachapelle +1
Unsupervised approaches to large language model (LLM) interpretability, such as sparse autoencoders (SAEs), offer a way to decode LLM activations into interpretable and, ideally, c…