Hobbhahn, Marius

Compute Trends Across Three Eras of Machine Learning

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN)

Parallel Computing and Optimization TechniquesArtificial Intelligence Applications

Cited by 363

Will we run out of data? Limits of LLM scaling based on human-generated data

Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L., & Hobbhahn, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 80

Large Language Models can Strategically Deceive their Users when Put Under Pressure

Scheurer, J., Balesni, M., & Hobbhahn, M. (2023) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs

Laine, R., Chughtai, B., Betley, J., Hariharan, K., Scheurer, J., Balesni, M., Hobbhahn, M., Meinke, A., & Evans, O. (2024) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 8

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs

Laine, R., Chughtai, B., Betley, J., Hariharan, K., Scheurer, J., Balesni, M., Hobbhahn, M., Meinke, A., & Evans, O. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingEthics and Social Impacts of AI

Cited by 8

Large Language Models Often Know When They Are Being Evaluated

Needham, J., Edkins, G., Pimpale, G., Bartsch, H., & Hobbhahn, M. (2025) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 0