| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPDO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 2735 | 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 | 1093 | 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 | 865 | 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 | 339 | 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 | 302 | 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 | 255 | 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 | 241 | 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 | 223 | 2021 |
| 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 | 180 | 2022 |
| Dawn Drain N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ... Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson …, 2021 | 152 | 2021 |
| Towards monosemanticity: Decomposing language models with dictionary learning T Bricken, A Templeton, J Batson, B Chen, A Jermyn, T Conerly, N Turner, ... Transformer Circuits Thread 2, 2023 | 139 | 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 | 124 | 2023 |
| 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 | 120 | 2022 |
| 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 | 109 | 2023 |
| Studying large language model generalization with influence functions R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ... arXiv preprint arXiv:2308.03296, 2023 | 75 | 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 | 68 | 2022 |
| Measuring faithfulness in chain-of-thought reasoning T Lanham, A Chen, A Radhakrishnan, B Steiner, C Denison, ... arXiv preprint arXiv:2307.13702, 2023 | 59 | 2023 |
| A mathematical framework for transformer circuits. Transformer Circuits Thread, 2021 N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ... | 58 | |
| 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 | 56 | 2022 |
| Evaluating large language models trained on code. arXiv 2021 M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374 10, 2021 | 41 | 2021 |
