Globerson, Amir

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Nightmare at test time: robust learning by feature deletion

Globerson, A., & Roweis, S. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06

Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning

Cited by 309

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Learning Canonical Representations for Scene Graph to Image Generation

Herzig, R., Bar, A., Xu, H., Chechik, G., Darrell, T., & Globerson, A. (2020) · Lecture Notes in Computer Science

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 92

LM vs LM: Detecting Factual Errors via Cross Examination

Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · arXiv

Topic ModelingMisinformation and Its Impacts

Cited by 85

BERTese: Learning to Speak to BERT

Haviv, A., Berant, J., & Globerson, A. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Topic ModelingInformation Retrieval and Search Behavior

Cited by 84

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

LM vs LM: Detecting Factual Errors via Cross Examination

Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingMisinformation and Its Impacts

Cited by 60

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Geva, M., Bastings, J., Filippova, K., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 53

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Geva, M., Bastings, J., Filippova, K., & Globerson, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

Learning to retrieve passages without supervision

Ram, O., Shachaf, G., Levy, O., Berant, J., & Globerson, A. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingInformation Retrieval and Search Behavior

Cited by 35

Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries

Biran, E., Gottesman, D., Yang, S., Geva, M., & Globerson, A. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0