| 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 | 24159 | 2020 |
| Language models are unsupervised multitask learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 9547 | 2019 |
| Deep speech 2: End-to-end speech recognition in english and mandarin D Amodei, S Ananthanarayanan, R Anubhai, J Bai, E Battenberg, C Case, ... International conference on machine learning, 173-182, 2016 | 3594 | 2016 |
| A cross-platform toolkit for mass spectrometry and proteomics MC Chambers, B Maclean, R Burke, D Amodei, DL Ruderman, ... Nature biotechnology 30 (10), 918-920, 2012 | 3127 | 2012 |
| Concrete problems in AI safety D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané arXiv preprint arXiv:1606.06565, 2016 | 2532 | 2016 |
| Deep reinforcement learning from human preferences PF Christiano, J Leike, T Brown, M Martic, S Legg, D Amodei Advances in neural information processing systems 30, 2017 | 2064 | 2017 |
| 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 |
| Scaling laws for neural language models J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ... arXiv preprint arXiv:2001.08361, 2020 | 1088 | 2020 |
| Learning to summarize with human feedback N Stiennon, L Ouyang, J Wu, D Ziegler, R Lowe, C Voss, A Radford, ... Advances in Neural Information Processing Systems 33, 3008-3021, 2020 | 1003 | 2020 |
| The malicious use of artificial intelligence: Forecasting, prevention, and mitigation M Brundage, S Avin, J Clark, H Toner, P Eckersley, B Garfinkel, A Dafoe, ... arXiv preprint arXiv:1802.07228, 2018 | 940 | 2018 |
| Fine-tuning language models from human preferences DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... arXiv preprint arXiv:1909.08593, 2019 | 845 | 2019 |
| 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 |
| AI and Compute D Amodei, D Hernandez, G Sastry, J Clark, G Brockman, I Sutskever | 411 | 2018 |
| Characterizing deformability and surface friction of cancer cells S Byun, S Son, D Amodei, N Cermak, J Shaw, JH Kang, VC Hecht, ... Proceedings of the National Academy of Sciences 110 (19), 7580-7585, 2013 | 387 | 2013 |
| Benchmarking safe exploration in deep reinforcement learning A Ray, J Achiam, D Amodei arXiv preprint arXiv:1910.01708 7 (1), 2, 2019 | 367 | 2019 |
| Building high-quality assay libraries for targeted analysis of SWATH MS data OT Schubert, LC Gillet, BC Collins, P Navarro, G Rosenberger, WE Wolski, ... Nature protocols 10 (3), 426-441, 2015 | 347 | 2015 |
| Reward learning from human preferences and demonstrations in atari B Ibarz, J Leike, T Pohlen, G Irving, S Legg, D Amodei Advances in neural information processing systems 31, 2018 | 330 | 2018 |
| Physical principles for scalable neural recording AH Marblestone, BM Zamft, YG Maguire, MG Shapiro, TR Cybulski, ... Frontiers in computational neuroscience 7, 137, 2013 | 297 | 2013 |
| Searching for collective behavior in a large network of sensory neurons G Tkačik, O Marre, D Amodei, E Schneidman, W Bialek, MJ Berry PLoS computational biology 10 (1), e1003408, 2014 | 282 | 2014 |
