Uesato, Jonathan

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

Taxonomy of Risks posed by Language Models

Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 764

Ethical and social risks of harm from Language Models

Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 477

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

GPT-4o System Card

OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 180

Improving alignment of dialogue agents via targeted human judgements

Glaese, A., McAleese, N., Trębacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M., Thacker, P., et al. (2022) · arXiv

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 134

Challenges in Detoxifying Language Models

Welbl, J., Glaese, A., Uesato, J., Dathathri, S., Mellor, J., Hendricks, L. A., Anderson, K., Kohli, P., Coppin, B., & Huang, P.-S. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 112

OpenAI o1 System Card

OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 50

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

Solving math word problems with process- and outcome-based feedback

Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., & Higgins, I. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 25

Reasoning Models Don't Always Say What They Think

Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., Somani, A., Hase, P., Wagner, M., Roger, F., et al. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 18

Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals

Shah, R., Varma, V., Kumar, R., Phuong, M., Krakovna, V., Uesato, J., & Kenton, Z. (2022) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 16