You can teach an LLM to explain what individual features in another LLM do by injecting those features and training it to verbalize their meaning—this works across different SAE dictionaries and even different models.
SAEVerbalizer trains language models to generate natural-language explanations for features extracted by sparse autoencoders (SAEs). Instead of observing model behavior to understand what features do, the system directly injects feature directions into the model and fine-tunes it to produce explanations, making the process faster and more scalable.