AI RESEARCH
Navigating the Concept Space of Language Models
arXiv CS.CL
•
ArXi:2603.23524v1 Announce Type: new Sparse autoencoders (SAEs) trained on large language model activations output thousands of features that enable mapping to human-interpretable concepts. The current practice for analyzing these features primarily relies on inspecting top-activating examples, manually browsing individual features, or performing semantic search on interested concepts, which makes exploratory discovery of concepts difficult at scale.