<ul><li>Feature circuits are how networks learn to combine input features to form complex patterns at higher levels.</li><li>In the context of Machine Learning, Sparse Autoencoders (SAEs) help disentangle the model's activations into a set of sparse features.</li><li>The study focuses on building a feature circuit in LLMs for a subject-verb agreement task.</li><li>Feature circuits provide insights into the decision-making process of a complex LLM and can be formed using SAEs.</li></ul>

Formulation of Feature Circuits with Sparse Autoencoders in LLM

Discover more