Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions. - Nadav Cohen 001; Or Sharir; Yoav Levine; Ronen Tamari; David Yakira; Amnon Shashua (CoRR, 2017)

Benefits of Depth for Long-Term Memory of Recurrent Networks. - Yoav Levine; Or Sharir; Amnon Shashua (ICLR (Workshop), 2018)

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design. - Yoav Levine; David Yakira; Nadav Cohen 001; Amnon Shashua (ICLR (Poster), 2018)

Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks. - Yoav Levine; Or Sharir; Nadav Cohen 001; Amnon Shashua (CoRR, 2018)

Deep autoregressive models for the efficient variational simulation of many-body quantum systems. - Or Sharir; Yoav Levine; Noam Wies; Giuseppe Carleo; Amnon Shashua (CoRR, 2019)

SenseBERT: Driving Some Sense into BERT. - Yoav Levine; Barak Lenz; Or Dagan; Dan Padnos; Or Sharir; Shai Shalev-Shwartz; Amnon Shashua; Yoav Shoham (CoRR, 2019)

SenseBERT: Driving Some Sense into BERT. - Yoav Levine; Barak Lenz; Or Dagan; Ori Ram; Dan Padnos; Or Sharir; Shai Shalev-Shwartz; Amnon Shashua; Yoav Shoham (ACL, 2020)

Limits to Depth Efficiencies of Self-Attention. - Yoav Levine; Noam Wies; Or Sharir; Hofit Bata; Amnon Shashua (NeurIPS, 2020)

PMI-Masking: Principled masking of correlated spans. - Yoav Levine; Barak Lenz; Opher Lieber; Omri Abend; Kevin Leyton-Brown; Moshe Tennenholtz; Yoav Shoham (ICLR, 2021)

Which transformer architecture fits my data? A vocabulary bottleneck in self-attention. - Noam Wies; Yoav Levine; Daniel Jannai; Amnon Shashua (ICML, 2021)

The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. - Yoav Levine; Noam Wies; Daniel Jannai; Dan Navon; Yedid Hoshen; Amnon Shashua (ICLR, 2022)

Standing on the Shoulders of Giant Frozen Language Models. - Yoav Levine; Itay Dalmedigos; Ori Ram; Yoel Zeldes; Daniel Jannai; Dor Muhlgay; Yoni Osin; Opher Lieber; Barak Lenz; Shai Shalev-Shwartz; Amnon Shashua; Kevin Leyton-Brown; et al. (CoRR, 2022)

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. - Ehud Karpas; Omri Abend; Yonatan Belinkov; Barak Lenz; Opher Lieber; Nir Ratner; Yoav Shoham; Hofit Bata; Yoav Levine; Kevin Leyton-Brown; Dor Muhlgay; Noam Rozen; et al. (CoRR, 2022)

Parallel Context Windows Improve In-Context Learning of Large Language Models. - Nir Ratner; Yoav Levine; Yonatan Belinkov; Ori Ram; Omri Abend; Ehud Karpas; Amnon Shashua; Kevin Leyton-Brown; Yoav Shoham (CoRR, 2022)

Parallel Context Windows for Large Language Models. - Nir Ratner; Yoav Levine; Yonatan Belinkov; Ori Ram; Inbal Magar; Omri Abend; Ehud Karpas; Amnon Shashua; Kevin Leyton-Brown; Yoav Shoham (ACL (1), 2023)

Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. - Noam Wies; Yoav Levine; Amnon Shashua (ICLR, 2023)

The Learnability of In-Context Learning. - Noam Wies; Yoav Levine; Amnon Shashua (NeurIPS, 2023)

Fundamental Limitations of Alignment in Large Language Models. - Yotam Wolf; Noam Wies; Yoav Levine; Amnon Shashua (CoRR, 2023)

Human or Not? A Gamified Approach to the Turing Test. - Daniel Jannai; Amos Meron; Barak Lenz; Yoav Levine; Yoav Shoham (CoRR, 2023)

In-Context Retrieval-Augmented Language Models. - Ori Ram; Yoav Levine; Itay Dalmedigos; Dor Muhlgay; Amnon Shashua; Kevin Leyton-Brown; Yoav Shoham (Trans. Assoc. Comput. Linguistics, 2023)

Generating Benchmarks for Factuality Evaluation of Language Models. - Dor Muhlgay; Ori Ram; Inbal Magar; Yoav Levine; Nir Ratner; Yonatan Belinkov; Omri Abend; Kevin Leyton-Brown; Amnon Shashua; Yoav Shoham (EACL (1), 2024)

STEER: Assessing the Economic Rationality of Large Language Models. - Narun Krishnamurthi Raman; Taylor Lundy; Samuel Joseph Amouyal; Yoav Levine; Kevin Leyton-Brown; Moshe Tennenholtz (ICML, 2024)

Fundamental Limitations of Alignment in Large Language Models. - Yotam Wolf; Noam Wies; Oshri Avnery; Yoav Levine; Amnon Shashua (ICML, 2024)

Tradeoffs Between Alignment and Helpfulness in Language Models. - Yotam Wolf; Noam Wies; Dorin Shteyman; Binyamin Rothberg; Yoav Levine; Amnon Shashua (CoRR, 2024)

Rationality Report Cards: Assessing the Economic Rationality of Large Language Models. - Narun K. Raman; Taylor Lundy; Samuel Joseph Amouyal; Yoav Levine; Kevin Leyton-Brown; Moshe Tennenholtz (CoRR, 2024)

Language model developers should report train-test overlap. - Andy K. Zhang; Kevin Klyman; Yifan Mai 001; Yoav Levine; Yian Zhang; Rishi Bommasani; Percy Liang (CoRR, 2024)

Artificial Expert Intelligence through PAC-reasoning. - Shai Shalev-Shwartz; Amnon Shashua; Gal Beniamini; Yoav Levine; Or Sharir; Noam Wies; Ido Ben-Shaul; Tomer Nussbaum; Shir Granot Peled (CoRR, 2024)

FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming. - Gal Beniamini; Yuval Dor; Alon Vinnikov; Shir Granot Peled; Or Weinstein; Or Sharir; Noam Wies; Tomer Nussbaum; Ido Ben-Shaul; Tomer Zekharya; Yoav Levine; Shai Shalev-Shwartz; et al. (CoRR, 2025)