Chen, Jilin

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 Reducing Gendered Correlations in Pre-trained Models

Webster, K., Wang, X., Tenney, I., Beutel, A., Pitler, E., Pavlick, E., Chen, J., Chi, E., & Petrov, S. (2020) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 195

Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements

Beutel, A., Chen, J., Doshi, T., Qian, H., Woodruff, A., Luu, C., Kreitmann, P., Bischof, J., & Chi, E. H. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 111

A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures

Baughan, A., Wang, X., Liu, A., Mercurio, A., Chen, J., & Ma, X. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

AI in Service InteractionsSpeech and dialogue systems

Cited by 32