How to Train BERT with an Academic Budget. - Peter Izsak; Moshe Berchansky; Omer Levy (EMNLP (1), 2021)

Optimizing Retrieval-augmented Reader Models via Token Elimination. - Moshe Berchansky; Peter Izsak; Avi Caciularu; Ido Dagan; Moshe Wasserblat (EMNLP, 2023)

CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity. - Moshe Berchansky; Daniel Fleischer; Moshe Wasserblat; Peter Izsak (EMNLP (Findings), 2024)

Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models. - Jonathan Mamou; Oren Pereg; Daniel Korat; Moshe Berchansky; Nadav Timor; Moshe Wasserblat; Roy Schwartz 001 (ENLSP, 2024)

Distributed Speculative Inference of Large Language Models is Provably Faster. - Nadav Timor; Jonathan Mamou; Oren Pereg; Moshe Berchansky; Daniel Korat; Moshe Wasserblat; Tomer Galanti; Michal Gordon; David Harel (ENLSP, 2024)

Accelerating Speculative Decoding using Dynamic Speculation Length. - Jonathan Mamou; Oren Pereg; Daniel Korat; Moshe Berchansky; Nadav Timor; Moshe Wasserblat; Roy Schwartz 001 (CoRR, 2024)

Distributed Speculative Inference of Large Language Models. - Nadav Timor; Jonathan Mamou; Daniel Korat; Moshe Berchansky; Oren Pereg; Moshe Wasserblat; Tomer Galanti; Michal Gordon; David Harel (CoRR, 2024)

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation. - Daniel Fleischer; Moshe Berchansky; Moshe Wasserblat; Peter Izsak (CoRR, 2024)

Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference. - Nadav Timor; Jonathan Mamou; Daniel Korat; Moshe Berchansky; Oren Pereg; Moshe Wasserblat; Tomer Galanti; Michal Gordon-Kiwkowitz; David Harel (ICLR, 2025)

Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies. - Nadav Timor; Jonathan Mamou; Daniel Korat; Moshe Berchansky; Oren Pereg; Gaurav Jain; Roy Schwartz 001; Moshe Wasserblat; David Harel (CoRR, 2025)

SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models. - Daniel Fleischer; Moshe Berchansky; Gad Markovits; Moshe Wasserblat (CoRR, 2025)