Publications – Yoav Levine

Yoav Levine

Yoav Levine - Publications

( See also Google Scholar )

In-Context Retrieval Augmented Language Models. TACL.
Ori Ram*, Yoav Levine*, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham.
Originally released on Feb’23.
Generating Benchmarks for Factuality Evaluation of Language Models. Preprint.
Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham.
Originally released on Jul’23.
Parallel Context Windows for Large Language Models. ACL 2023.
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham.
Originally released on Dec’22.
Human or Not? A Gamified Approach to the Turing Test. Preprint.
Daniel Jannai, Amos Meron, Barak Lenz, Yoav Levine, Yoav Shoham.
Originally released on May’23.
Fundamental Limitations of Alignment in Large Language Models. Preprint.
Yotam Wolf*, Noam Wies*, Yoav Levine, and Amnon Shashua.
Originally released on Apr’23.
The Learnability of In-Context Learning. NeurIPS 2023.
Noam Wies, Yoav Levine, and Amnon Shashua.
Originally released on Mar’23.
Huge Frozen Language Models as Readers for Open-Domain Question Answering. ICML 2022 workshop.
Yoav Levine*, Ori Ram*, Daniel Jannai, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham

Standing on the Shoulders of Giant Frozen Language Models. Preprint.
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, and Yoav Shoham.
Originally released on Apr’22.
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. Preprint.
Noam Wies, Yoav Levine, and Amnon Shashua.
Originally released on Apr’22.

The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. Spotlight paper, ICLR 2021.
Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua.
Originally released on Oct’21.

Which Transformer Architecture Fits my Data? A Vocabulary Bottleneck in Self-Attention. ICML 2021.
Noam Wies, Yoav Levine, Daniel Jannai, and Amnon Shashua.
Originally released on May’21.

PMI-Masking: Principled Masking of Correlated Spans. Spotlight paper, ICLR 2021.
Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, and Yoav Shoham.
Originally released on Oct’20.
The Depth-to-Width Interplay in Self-Attention. NeurIPS 2020.
Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, and Amnon Shashua.
Originally released on June’20.

SenseBERT: Driving Some Sense into BERT. ACL 2020.
Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, and Yoav Shoham. Originally released on Aug’19.

Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems. Physical Review Letters (PRL) 124, 020503 (2019).
Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua.
Originally released on Feb’19.

Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks.
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua.
Benefits of Depth for Long-Term Memory of Recurrent Networks. ICLR 2018 Workshop. Yoav Levine, Or Sharir, and Amnon Shashua. Originally released on Oct’17.

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design. ICLR 2018. Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua. Originally released on Apr’17.
Realizing Topological Superconductivity with Superlattices. Phys. Rev. B 96, 165147.
Yoav Levine, Arbel Haim, and Yuval Oreg.
Originally released on July’17.

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions. Intel Collaborative Researceh Institute Special Issue on Deep Learning Theory.
Nadav Cohen, Or Sharir, Yoav Levine, Ronen Tamari, David Yakira, and Amnon Shashua. Originally released on May’17.

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. Preprint.
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, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tenenholtz.
Originally released on May’22.

Book Chapters:

Tensors for deep learning theory: Analyzing deep learning architectures via tensorization. Elsevier, 2022.
Yoav Levine, Noam Wies, Or Sharir, Nadav Cohen, and Amnon Shashua.

Mathematics of Deep Learning: Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks. Cambridge University Press, 2022.
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua.

Yoav Levine

Yoav Levine - Publications

( See also Google Scholar )

In-Context Retrieval Augmented Language Models. TACL.
Ori Ram*, Yoav Levine*, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham.
Originally released on Feb’23.
Generating Benchmarks for Factuality Evaluation of Language Models. Preprint. Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham. Originally released on Jul’23.
Parallel Context Windows for Large Language Models. ACL 2023.
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham.
Originally released on Dec’22.
Human or Not? A Gamified Approach to the Turing Test. Preprint.
Daniel Jannai, Amos Meron, Barak Lenz, Yoav Levine, Yoav Shoham. Originally released on May’23.
Fundamental Limitations of Alignment in Large Language Models. Preprint. Yotam Wolf*, Noam Wies*, Yoav Levine, and Amnon Shashua. Originally released on Apr’23.
The Learnability of In-Context Learning. Preprint. Noam Wies, Yoav Levine, and Amnon Shashua. Originally released on Mar’23.
Huge Frozen Language Models as Readers for Open-Domain Question Answering. ICML 2022 workshop.
Yoav Levine*, Ori Ram*, Daniel Jannai, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham

Standing on the Shoulders of Giant Frozen Language Models. Preprint.
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, and Yoav Shoham. Originally released on Apr’22.
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. Preprint.
Noam Wies, Yoav Levine, and Amnon Shashua.
Originally released on Apr’22.

The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. Spotlight paper, ICLR 2021.
Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua.
Originally released on Oct’21.

Which Transformer Architecture Fits my Data? A Vocabulary Bottleneck in Self-Attention. ICML 2021.
Noam Wies, Yoav Levine, Daniel Jannai, and Amnon Shashua.
Originally released on May’21.

PMI-Masking: Principled Masking of Correlated Spans. Spotlight paper, ICLR 2021.
Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, and Yoav Shoham.
Originally released on Oct’20.
The Depth-to-Width Interplay in Self-Attention. NeurIPS 2020.
Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, and Amnon Shashua.
Originally released on June’20.

SenseBERT: Driving Some Sense into BERT. ACL 2020.
Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, and Yoav Shoham. Originally released on Aug’19.

Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems. Physical Review Letters (PRL) 124, 020503 (2019).
Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua.
Originally released on Feb’19.

Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks.
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua.
Benefits of Depth for Long-Term Memory of Recurrent Networks. ICLR 2018 Workshop. Yoav Levine, Or Sharir, and Amnon Shashua. Originally released on Oct’17.

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design. ICLR 2018. Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua. Originally released on Apr’17.
Realizing Topological Superconductivity with Superlattices. Phys. Rev. B 96, 165147.
Yoav Levine, Arbel Haim, and Yuval Oreg.
Originally released on July’17.

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions. Intel Collaborative Researceh Institute Special Issue on Deep Learning Theory.
Nadav Cohen, Or Sharir, Yoav Levine, Ronen Tamari, David Yakira, and Amnon Shashua. Originally released on May’17.

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. Preprint.
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, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tenenholtz.
Originally released on May’22.

Book Chapters:

Tensors for deep learning theory: Analyzing deep learning architectures via tensorization. Elsevier, 2022.
Yoav Levine, Noam Wies, Or Sharir, Nadav Cohen, and Amnon Shashua.

Mathematics of Deep Learning: Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks. Cambridge University Press, 2022.
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua.