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Notes on sparse autoencoders
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Multimodal Model Diffing for Feature Discovery and Control
Researchers introduce a framework called MMDiff to help identify, control, and audit the internal features of Multimodal Large Language Models (MLLMs). The framework is designed to isolate which features are altered by…
C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
The paper introduces C$^2$R, a method to improve Sparse Autoencoders (SAEs) used for interpreting large language models. The main challenge with SAEs is scaling to large dictionaries, which leads to issues like feature…
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