Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability
Adam Karvonen, Can Rager, Johnny Lin +12
Sparse autoencoders (SAEs) are a popular technique for interpreting language model activations, and there is extensive recent work on improving SAE effectiveness. However, most pri…
cs.LG2024
Applying sparse autoencoders to unlearn knowledge in language models
Eoin Farrell, Yeu-Tong Lau, Arthur Conmy
We investigate whether sparse autoencoders (SAEs) can be used to remove knowledge from language models. We use the biology subset of the Weapons of Mass Destruction Proxy dataset a…