Sleight, Henry

Best-of-N Jailbreaking

Hughes, J., Price, S., Lynch, A., Schaeffer, R., Barez, F., Koyejo, S., Sleight, H., Jones, E., Perez, E., & Sharma, M. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 129

Looking Inward: Language Models Can Learn About Themselves by Introspection

Binder, F. J., Chua, J., Korbak, T., Sleight, H., Hughes, J., Long, R., Perez, E., Turpin, M., & Evans, O. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Philosophy and Theoretical Science

Cited by 20

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., et al. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisTopic Modeling

Cited by 15

Persona Vectors: Monitoring and Controlling Character Traits in Language Models

Chen, R., Arditi, A., Sleight, H., Evans, O., & Lindsey, J. (2025) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 7

Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs

Sheshadri, A., Ewart, A., Guo, P., Lynch, A., Wu, C., Hebbar, V., Sleight, H., Stickland, A. C., Perez, E., Hadfield-Menell, D., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 3

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

Wen, J., Hebbar, V., Larson, C., Bhatt, A., Radhakrishnan, A., Sharma, M., Sleight, H., Feng, S., He, H., Perez, E., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1

Unsupervised Elicitation of Language Models

Wen, J., Ankner, Z., Somani, A., Hase, P., Marks, S., Goldman-Wetzler, J., Petrini, L., Sleight, H., Burns, C., He, H., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment

Wichers, N., Ebtekar, A., Azarbal, A., Gillioz, V., Ye, C., Ryd, E., Rathi, N., Sleight, H., Mallen, A., Roger, F., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 0