Lars Kai Hansen

This paper demonstrates that Sparse Autoencoders (SAEs) can effectively steer Large Language Models (LLMs) on the AxBench benchmark, achieving performance comparable to LoRA baselines when combined with a novel supervised feature selection pipeline.