6 citations · 21 across the 21 of their papers we have counts for
6 papers · 2 filters
Efficient Decoding Methods for Language Models on Encrypted Data
Matan Avitan, Moran Baruch, Nir Drucker +2
Large language models (LLMs) power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption (HE) enables computati…
Differential Mamba
Nadav Schneider, Itamar Zimerman, Eliya Nachmani
Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by prom…
Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs
Roy Eisenstadt, Itamar Zimerman, Lior Wolf
Recently, techniques such as explicit structured reasoning have demonstrated strong test-time scaling behavior by enforcing a separation between the model's internal "thinking" pro…
Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability
Yarden Bakish, Itamar Zimerman, Hila Chefer +1
The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise…
Overflow Prevention Enhances Long-Context Recurrent LLMs
Assaf Ben-Kish, Itamar Zimerman, M. Jehanzeb Mirza +4
A recent trend in LLMs is developing recurrent sub-quadratic models that improve long-context processing efficiency. We investigate leading large long-context models, focusing on h…
On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach
Edo Cohen-Karlik, Itamar Zimerman, Liane Galanti +3
Recent advances in efficient sequence modeling have introduced selective state-space layers, a key component of the Mamba architecture, which have demonstrated remarkable success i…