[AARR] Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

The Align AI Research Review discusses a paper that explores understanding and interpreting the cognitive processes of AI using sparse autoencoders to extract interpretable features from large language models like Claude 3 Sonnet. By training these autoencoders on substantial datasets, thousands of features utilized by the model to process information were identified. The researchers discovered a systematic correlation between the overall incidence of a concept in the training data and the dictionary size required to resolve a corresponding feature. They also demonstrated that manipulating the activations of specific features could consistently induce the model to exhibit or refrain from specific behaviors, providing valuable insights into the model's internal representations and behaviors.