| Language models are few-shot learners T Brown, B Mann, N Ryder, M Subbiah, JD Kaplan, P Dhariwal, ... Advances in neural information processing systems 33, 1877-1901, 2020 | 29966* | 2020 |
| Scaling laws for neural language models J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ... arXiv preprint arXiv:2001.08361, 2020 | 2299 | 2020 |
| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 1950 | 2021 |
| Training a helpful and harmless assistant with reinforcement learning from human feedback Y Bai, A Jones, K Ndousse, A Askell, A Chen, N DasSarma, D Drain, ... arXiv preprint arXiv:2204.05862, 2022 | 663 | 2022 |
| Constitutional ai: Harmlessness from ai feedback Y Bai, S Kadavath, S Kundu, A Askell, J Kernion, A Jones, A Chen, ... arXiv preprint arXiv:2212.08073, 2022 | 568 | 2022 |
| A stereoscopic look into the bulk B Czech, L Lamprou, S McCandlish, B Mosk, J Sully Journal of High Energy Physics 2016 (7), 1-47, 2016 | 243 | 2016 |
| Integral geometry and holography B Czech, L Lamprou, S McCandlish, J Sully Journal of High Energy Physics 2015 (10), 1-41, 2015 | 237 | 2015 |
| Scaling laws for autoregressive generative modeling T Henighan, J Kaplan, M Katz, M Chen, C Hesse, J Jackson, H Jun, ... arXiv preprint arXiv:2010.14701, 2020 | 231 | 2020 |
| Spontaneous segregation of self-propelled particles with different motilities SR McCandlish, A Baskaran, MF Hagan Soft Matter 8 (8), 2527-2534, 2012 | 227 | 2012 |
| Language models (mostly) know what they know S Kadavath, T Conerly, A Askell, T Henighan, D Drain, E Perez, ... arXiv preprint arXiv:2207.05221, 2022 | 219 | 2022 |
| Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned D Ganguli, L Lovitt, J Kernion, A Askell, Y Bai, S Kadavath, B Mann, ... arXiv preprint arXiv:2209.07858, 2022 | 210 | 2022 |
| A general language assistant as a laboratory for alignment A Askell, Y Bai, A Chen, D Drain, D Ganguli, T Henighan, A Jones, ... arXiv preprint arXiv:2112.00861, 2021 | 208 | 2021 |
| An empirical model of large-batch training S McCandlish, J Kaplan, D Amodei, OAID Team arXiv preprint arXiv:1812.06162, 2018 | 198 | 2018 |
| In-context learning and induction heads C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ... arXiv preprint arXiv:2209.11895, 2022 | 184 | 2022 |
| Predictability and surprise in large generative models D Ganguli, D Hernandez, L Lovitt, A Askell, Y Bai, A Chen, T Conerly, ... Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 169 | 2022 |
| Tensor networks from kinematic space B Czech, L Lamprou, S McCandlish, J Sully Journal of High Energy Physics 2016 (7), 1-38, 2016 | 165 | 2016 |
| A mathematical framework for transformer circuits N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ... Transformer Circuits Thread 1, 1, 2021 | 146 | 2021 |
| Toy models of superposition N Elhage, T Hume, C Olsson, N Schiefer, T Henighan, S Kravec, ... arXiv preprint arXiv:2209.10652, 2022 | 137 | 2022 |
| Language models are few-shot learners. arXiv TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... Computer Science, Computation and Language, 2005 | 137 | 2005 |
| Scaling laws for transfer D Hernandez, J Kaplan, T Henighan, S McCandlish arXiv preprint arXiv:2102.01293, 2021 | 125 | 2021 |
Jared KaplanJohns Hopkins University & AnthropicVerified email at pha.jhu.edu
