1 citations · 1 across the 7 of their papers we have counts for
17 papers
Concept-Aware Batch Sampling Improves Language-Image Pretraining
Adhiraj Ghosh, Vishaal Udandarao, Thao Nguyen +7
What data should a vision-language model be trained on? To answer this question, many data curation efforts center on the quality of a dataset. However, most of these existing meth…
Solving Spatial Supersensing Without Spatial Supersensing
Vishaal Udandarao, Shyamgopal Karthik, Surabhi S. Nath +3
Cambrian-S aims to take the first steps towards improving video world models with spatial supersensing by introducing (i) two benchmarks, VSI-Super-Recall (VSR) and VSI-Super-Count…
Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions
Tobias Schmidt, Steffen Schneider, Matthias Bethge
We propose Equivariance by Contrast (EbC) to learn equivariant embeddings from observation pairs , where is drawn from a finite group acting o…
Mapping Post-Training Forgetting in Language Models at Scale
Jackson Harmon, Andreas Hochlehnert, Matthias Bethge +1
Scaled post-training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting…
Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLMs
Alexander Panfilov, Evgenii Kortukov, Kristina Nikolić +6
Large language model (LLM) developers aim for their models to be honest, helpful, and harmless. However, when faced with malicious requests, models are trained to refuse, sacrifici…
AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Ori Press, Brandon Amos, Haoyu Zhao +21
Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming…