papers

Publications (8)

cs.CV2025

Do generative video models understand physical principles?

Saman Motamed, Laura Culp, Kevin Swersky +2

AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world…

cs.CV2022

Testing GLOM's ability to infer wholes from ambiguous parts

Laura Culp, Sara Sabour, Geoffrey E. Hinton

The GLOM architecture proposed by Hinton [2021] is a recurrent neural network for parsing an image into a hierarchy of wholes and parts. When a part is ambiguous, GLOM assumes that…

cs.CL2025

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Rohan Anil, Sebastian Borgeaud +1340

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consist…

cs.LG2024

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…

cs.CY2025

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Irina Jurenka, Markus Kunesch, Kevin R. McKee +71

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about…

cs.CL2024

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability

Jiri Hron, Laura Culp, Gamaleldin Elsayed +28

While many capabilities of language models (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come i…

cs.CL2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi +27

We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic…

cs.CV2022

Robust and Efficient Medical Imaging with Self-Supervision

Shekoofeh Azizi, Laura Culp, Jan Freyberg +31

Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optima…