Exploring Text Classification Models with Sparse Autoencoders
Techniques for exploring the relationships between the SAE's features and the model's predictions and errors are presented and integrated into SAEfarer, a tool for analyzing concepts learned by text classification LMs.
Daniel Kerrigan, Brian Barr, Enrico Bertini
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