Turpin, Miles

Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 262

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

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 19

Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought

Chua, J., Rees, E., Batra, H., Bowman, S. R., Michael, J., Perez, E., & Turpin, M. (2024) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 0