Michael, Julian

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Topic ModelingNatural Language Processing Techniques

Cited by 4,222

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,051

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 562

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

Remote Labor Index: Measuring AI Automation of Remote Work

Mazeika, M., Gatti, A., Menghini, C., Sehwag, U. M., Singhal, S., Orlovskiy, Y., Basart, S., Sharma, M., Peskoff, D., Lau, E., et al. (2025) · arXiv

Artificial Intelligence ApplicationsLabor market dynamics and wage inequality

Cited by 44

GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., & Bowman, S. R. (2023) · arXiv

Topic ModelingExpert finding and Q&A systems

Cited by 30

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

International AI Safety Report

Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., Choi, Y., Fox, P., Garfinkel, B., Goldfarb, D., et al. (2025) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 16

Debate Helps Supervise Unreliable Experts

Michael, J., Mahdi, S., Rein, D., Petty, J., Dirani, J., Padmakumar, V., & Bowman, S. R. (2023) · arXiv

Epistemology, Ethics, and MetaphysicsExpert finding and Q&A systems

Cited by 8

We're Afraid Language Models Aren't Modeling Ambiguity

Liu, A., Wu, Z., Michael, J., Suhr, A., West, P., Koller, A., Swayamdipta, S., Smith, N. A., & Choi, Y. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 3

A Red Teaming Roadmap Towards System-Level Safety

Wang, Z., Knight, C. Q., Kritz, J., Primack, W. E., & Michael, J. (2025) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 1

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