109 citations · 125 across the 3 of their papers we have counts for
4 papers
LIFT: Scoped Constraints for Evaluating and Training Instruction Following
Eran Goldstein, Yuval Belfer, Yonatan Belinkov +1
Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmaticall…
Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
Jamba Team, Barak Lenz, Alan Arazi +58
We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing hi…
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the…
Discriminative Active Learning
Daniel Gissin, Shai Shalev-Shwartz
We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learn…