Irving, Geoffrey

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 9,408

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

Fine-Tuning Language Models from Human Preferences

Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., & Irving, G. (2019) · arXiv

Topic ModelingReinforcement Learning in Robotics

Cited by 413

Improving language models by retrieving from trillions of tokens

Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 382

Red Teaming Language Models with Language Models

Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 363

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

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

Reward learning from human preferences and demonstrations in Atari

Ibarz, B., Leike, J., Pohlen, T., Irving, G., Legg, S., & Amodei, D. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 117

AI safety via debate

Irving, G., Christiano, P., & Amodei, D. (2018) · arXiv

Ethics and Social Impacts of AIReinforcement Learning in Robotics

Cited by 71

Teaching language models to support answers with verified quotes

Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., Glaese, M., Young, S., Campbell-Gillingham, L., Irving, G., et al. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 53

Alignment of Language Agents

Kenton, Z., Everitt, T., Weidinger, L., Gabriel, I., Mikulik, V., & Irving, G. (2021) · arXiv

Ethics and Social Impacts of AISpeech and dialogue systems

Cited by 45

Accelerating Large Language Model Decoding with Speculative Sampling

Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., & Jumper, J. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 40

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

Uncertainty Estimation for Language Reward Models

Gleave, A., & Irving, G. (2022) · arXiv

Machine Learning and AlgorithmsTopic Modeling

Cited by 6

Open technical problems in open-weight ai model risk management

Casper, S., O'Brien, K., Longpre, S., Seger, E., Klyman, K., Bommasani, R., Nrusimha, A., Shumailov, I., Mindermann, S., Basart, S., et al. (2025) · SSRN Electronic Journal

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 3

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs

O'Brien, K., Casper, S., Anthony, Q., Korbak, T., Kirk, R., Davies, X., Mishra, I., Irving, G., Gal, Y., & Biderman, S. (2025) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 0