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.CV2025
Interpreting the linear structure of vision-language model embedding spaces
Isabel Papadimitriou, Huangyuan Su, Thomas Fel +2
Vision-language models encode images and text in a joint space, minimizing the distance between corresponding image and text pairs. How are language and images organized in this jo…
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…