Stokowiec, Wojciech

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

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

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

Internet-augmented language models through few-shot prompting for open-domain question answering

Lazaridou, A., Gribovskaya, E., Stokowiec, W., & Grigorev, N. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 64