10 papers
Training Transformers for KV Cache Compressibility
Yoav Gelberg, Yam Eitan, Michael Bronstein +2
Long-context language modeling is increasingly constrained by the Key-Value (KV) cache, whose memory and decode-time access costs scale linearly with the prefix length. This bottle…
Muon is Not That Special: Random or Inverted Spectra Work Just as Well
Zakhar Shumaylov, Nathaël Da Costa, Peter Zaika +6
The recent empirical success of the Muon optimizer has renewed interest in non-Euclidean optimization, typically justified by similarities with second-order methods, and linear min…
Iterative Deployment Improves Planning Skills in LLMs
Augusto B. Corrêa, Yoav Gelberg, Luckeciano C. Melo +3
We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change…
Extending the Context of Pretrained LLMs by Dropping Their Positional Embeddings
Yoav Gelberg, Koshi Eguchi, Takuya Akiba +1
So far, expensive finetuning beyond the pretraining sequence length has been a requirement for effectively extending the context of language models (LM). In this work, we break thi…
On The Expressive Power of GNN Derivatives
Yam Eitan, Moshe Eliasof, Yoav Gelberg +3
Despite significant advances in Graph Neural Networks (GNNs), their limited expressivity remains a fundamental challenge. Research on GNN expressivity has produced many expressive…
GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon +5
Gradients of neural networks encode valuable information for optimization, editing, and analysis of models. Therefore, practitioners often treat gradients as inputs to task-specifi…
Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributions
Guy Bar-Shalom, Fabrizio Frasca, Derek Lim +5
The automated detection of hallucinations and training data contamination is pivotal to the safe deployment of Large Language Models (LLMs). These tasks are particularly challengin…
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models
Theo Putterman, Derek Lim, Yoav Gelberg +2
Low-rank adaptations (LoRAs) have revolutionized the finetuning of large foundation models, enabling efficient adaptation even with limited computational resources. The resulting p…
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks
Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk +1
Weight space symmetries in neural network architectures, such as permutation symmetries in MLPs, give rise to Bayesian neural network (BNN) posteriors with many equivalent modes. T…
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom +3
Topological deep learning (TDL) is a rapidly growing field that seeks to leverage topological structure in data and facilitate learning from data supported on topological objects,…