4 papers
Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry
Jennifer Meng Lu, Ruochen Zhang, Isabelle Lee +3
Humans cannot always intuit what scenarios are most challenging to LLMs. Hoping to capture challenging edge cases, developers either design problems to be difficult for humans or c…
Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention
Jing Huang, Daniel Wurgaft, Rachit Bansal +6
Larger models learn tasks smaller models do not. What drives this phenomenon? We develop a simple phenomenological argument that power-law scaling already suggests that a larger mo…
Random Scaling of Emergent Capabilities
Rosie Zhao, Tian Qin, David Alvarez-Melis +2
Language models famously improve under a smooth scaling law, but some specific capabilities exhibit sudden breakthroughs in performance. Advocates of "emergence" view these capabil…
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…