Sparse-autoencoder

This summary explores the concept of feature universality in large language models (LLMs) using sparse autoencoders (SAEs), as presented in 'Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models' (Lan et al., 2024). The research aims to determine if different LLMs develop similar internal representations of concepts within their intermediate layers.