Kramár, János

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Kramár, J., Lieberum, T., Shah, R., & Nanda, N. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 120

On scalable oversight with weak LLMs judging strong LLMs

Kenton, Z., Siegel, N. Y., Kramár, J., Brown-Cohen, J., Albanie, S., Bulian, J., Agarwal, R., Lindner, D., Tang, Y., Goodman, N. D., et al. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 66

Learning Reciprocity in Complex Sequential Social Dilemmas

Eccles, T., Hughes, E., Kramár, J., Wheelwright, S., & Leibo, J. Z. (2019) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 19

Tracr: Compiled Transformers as a Laboratory for Interpretability

Lindner, D., Kramár, J., Farquhar, S., Rahtz, M., McGrath, T., & Mikulik, V. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 8

Improving Dictionary Learning with Gated Sparse Autoencoders

Rajamanoharan, S., Conmy, A., Smith, L., Lieberum, T., Varma, V., Kramár, J., Shah, R., & Nanda, N. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 3

Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Lieberum, T., Rajamanoharan, S., Conmy, A., Smith, L., Sonnerat, N., Varma, V., Kramár, J., Dragan, A., Shah, R., & Nanda, N. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 2