| 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 | 1453 | 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 | 1139 | 2022 |
| Graphcodebert: Pre-training code representations with data flow D Guo, S Ren, S Lu, Z Feng, D Tang, S Liu, L Zhou, N Duan, ... arXiv preprint arXiv:2009.08366, 2020 | 997 | 2020 |
| Codexglue: A machine learning benchmark dataset for code understanding and generation S Lu, D Guo, S Ren, J Huang, A Svyatkovskiy, A Blanco, C Clement, ... arXiv preprint arXiv:2102.04664, 2021 | 823 | 2021 |
| 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 | 416 | 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 | 359 | 2021 |
| In-context learning and induction heads C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ... arXiv preprint arXiv:2209.11895, 2022 | 332 | 2022 |
| 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), 12, 2021 | 280 | 2021 |
| 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 | 278 | 2022 |
| Toy models of superposition N Elhage, T Hume, C Olsson, N Schiefer, T Henighan, S Kravec, ... arXiv preprint arXiv:2209.10652, 2022 | 269 | 2022 |
| Discovering language model behaviors with model-written evaluations E Perez, S Ringer, K Lukošiūtė, K Nguyen, E Chen, S Heiner, C Pettit, ... arXiv preprint arXiv:2212.09251, 2022 | 219 | 2022 |
| 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 | 143 | 2022 |
| 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 | 132 | 2023 |
| Measuring progress on scalable oversight for large language models SR Bowman, J Hyun, E Perez, E Chen, C Pettit, S Heiner, K Lukošiūtė, ... arXiv preprint arXiv:2211.03540, 2022 | 86 | 2022 |
| PyMT5: multi-mode translation of natural language and Python code with transformers CB Clement, D Drain, J Timcheck, A Svyatkovskiy, N Sundaresan arXiv preprint arXiv:2010.03150, 2020 | 84 | 2020 |
| A mathematical framework for transformer circuits. Transformer Circuits Thread, 2021 N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ... | 73 | |
| Unit test case generation with transformers and focal context M Tufano, D Drain, A Svyatkovskiy, SK Deng, N Sundaresan arXiv preprint arXiv:2009.05617, 2020 | 64 | 2020 |
| Scaling laws and interpretability of learning from repeated data D Hernandez, T Brown, T Conerly, N DasSarma, D Drain, S El-Showk, ... arXiv preprint arXiv:2205.10487, 2022 | 63 | 2022 |
| Generating bug-fixes using pretrained transformers D Drain, C Wu, A Svyatkovskiy, N Sundaresan Proceedings of the 5th ACM SIGPLAN International Symposium on Machine …, 2021 | 58 | 2021 |
| Generating accurate assert statements for unit test cases using pretrained transformers M Tufano, D Drain, A Svyatkovskiy, N Sundaresan Proceedings of the 3rd ACM/IEEE International Conference on Automation of …, 2022 | 56 | 2022 |
