Glaese, Amelia

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

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

PaperBench: Evaluating AI's Ability to Replicate AI Research

Starace, G., Jaffe, O., Sherburn, D., Aung, J., Chan, J. S., Maksin, L., Dias, R., Mays, E., Kinsella, B., Thompson, W., et al. (2025) · arXiv

Artificial Intelligence ApplicationsSoftware Engineering Research

Cited by 310

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

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Patwardhan, T., Dias, R., Proehl, E., Kim, G., Wang, M., Watkins, O., Fishman, S. P., Aljubeh, M., Thacker, P., Fauconnet, L., et al. (2025) · arXiv

Artificial Intelligence ApplicationsTopic Modeling

Cited by 189

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

Measuring short-form factuality in large language models

Wei, J., Karina, N., Chung, H. W., Jiao, Y. J., Papay, S., Glaese, A., Schulman, J., & Fedus, W. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Wei, J., Sun, Z., Papay, S., McKinney, S., Han, J., Fulford, I., Chung, H. W., Passos, A. T., Fedus, W., & Glaese, A. (2025) · arXiv

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 2