Kassner, Nora

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

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

Measuring and improving consistency in pretrained language models

Elazar, Y., Kassner, N., Ravfogel, S., Ravichander, A., Hovy, E., Schütze, H., & Goldberg, Y. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 220

Language Models As or For Knowledge Bases

Razniewski, S., Yates, A., Kassner, N., & Weikum, G. (2021) · arXiv

Topic ModelingSemantic Web and Ontologies

Cited by 16

Do Large Language Models Latently Perform Multi-Hop Reasoning?

Yang, S., Gribovskaya, E., Kassner, N., Geva, M., & Riedel, S. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0