| Towards measuring the representation of subjective global opinions in language models E Durmus, K Nguyen, TI Liao, N Schiefer, A Askell, A Bakhtin, C Chen, ... arXiv preprint arXiv:2306.16388, 2023 | 179 | 2023 |
| The capacity for moral self-correction in large language models D Ganguli, A Askell, N Schiefer, TI Liao, K Lukošiūtė, A Chen, A Goldie, ... arXiv preprint arXiv:2302.07459, 2023 | 157 | 2023 |
| Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning TI Liao, R Taori, ID Raji, L Schmidt Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 130 | 2021 |
| Data-efficient Learning of Morphology and Controller for a Microrobot TI Liao, G Wang, B Yang, R Lee, K Pister, S Levine, R Calandra 2019 International Conference on Robotics and Automation (ICRA), 2488-2494, 2019 | 72 | 2019 |
| Collective constitutional ai: Aligning a language model with public input S Huang, D Siddarth, L Lovitt, TI Liao, E Durmus, A Tamkin, D Ganguli Proceedings of the 2024 ACM Conference on Fairness, Accountability, and …, 2024 | 38* | 2024 |
| Ecosystem graphs: The social footprint of foundation models R Bommasani, D Soylu, TI Liao, KA Creel, P Liang arXiv preprint arXiv:2303.15772, 2023 | 34 | 2023 |
| Specific versus general principles for constitutional ai S Kundu, Y Bai, S Kadavath, A Askell, A Callahan, A Chen, A Goldie, ... arXiv preprint arXiv:2310.13798, 2023 | 30 | 2023 |
| Why external validity matters for machine learning evaluation: Motivation and open problems TI Liao, R Taori, L Schmidt | 2 | 2022 |
| In a forward direction: Analyzing distribution shifts in machine translation test sets over time T Liao, B Recht, L Schmidt ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning, 2020 | 1 | 2020 |
