3 papers
cs.CL2025
Vocabulary embeddings organize linguistic structure early in language model training
Isabel Papadimitriou, Jacob Prince
Large language models (LLMs) work by manipulating the geometry of input embedding vectors over multiple layers. Here, we ask: how are the input vocabulary representations of langua…
cs.LG2025
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
Muquan Yu, Mu Nan, Hossein Adeli +6
Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-s…
cs.CV2025
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince +7
Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dicti…