Farquhar, Sebastian

Detecting hallucinations in large language models using semantic entropy

Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 934

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 849

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 849

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Kuhn, L., Gal, Y., & Farquhar, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 700

Model evaluation for extreme risks

Shevlane, T., Farquhar, S., Garfinkel, B., Phuong, M., Whittlestone, J., Leung, J., Kokotajlo, D., Marchal, N., Anderljung, M., Kolt, N., et al. (2023) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 66

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Farquhar, S., Osborne, M., & Gal, Y. (2019) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 43

Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

Mindermann, S., Brauner, J., Razzak, M., Sharma, M., Kirsch, A., Xu, W., Höltgen, B., Gomez, A. N., Morisot, A., Farquhar, S., et al. (2022) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 28

Discovering agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2023) · Artificial Intelligence

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 17

Evaluating Frontier Models for Dangerous Capabilities

Phuong, M., Aitchison, M., Catt, E., Cogan, S., Kaskasoli, A., Krakovna, V., Lindner, D., Rahtz, M., Assael, Y., Hodkinson, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

Prediction-Oriented Bayesian Active Learning

Smith, F. B., Kirsch, A., Farquhar, S., Gal, Y., Foster, A., & Rainforth, T. (2023) · arXiv

Machine Learning and AlgorithmsGaussian Processes and Bayesian Inference

Cited by 10

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

Discovering Agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2022) · arXiv

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 7