Khalman, Misha

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

Calibrating Sequence likelihood Improves Conditional Language Generation

Zhao, Y., Khalman, M., Joshi, R., Narayan, S., Saleh, M., & Liu, P. J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 42

SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Zhao, Y., Joshi, R., Liu, T., Khalman, M., Saleh, M., & Liu, P. J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11