In this paper, the authors propose a method to identify and interpret features in large language models (LLMs) using sparse autoencoders (SAEs). They demonstrate that these features can be used for various applications such as model editing, feature ablation, searching for specific features, and ensuring safety. The main takeaway from this paper is the potential of SAEs to provide a better understanding of LLMs' inner workings, which could lead to more robust and safer models in the future.