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 | 40079 | 2020 |
Learning Transferable Visual Models From Natural Language Supervision A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... https://cdn.openai.com/papers …, 2021 | 28718 | 2021 |
Language Models are Unsupervised Multitask Learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever Technical report, OpenAi, 2019 | 26416* | 2019 |
Proximal policy optimization algorithms J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov arXiv preprint arXiv:1707.06347, 2017 | 23532 | 2017 |
Unsupervised representation learning with deep convolutional generative adversarial networks A Radford arXiv preprint arXiv:1511.06434, 2015 | 19614 | 2015 |
Improving language understanding by generative pre-training A Radford | 13385 | 2018 |
Improved techniques for training gans T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen Advances in neural information processing systems 29, 2016 | 11511 | 2016 |
Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 7886 | 2023 |
Zero-shot text-to-image generation A Ramesh, M Pavlov, G Goh, S Gray, C Voss, A Radford, M Chen, ... International conference on machine learning, 8821-8831, 2021 | 5544 | 2021 |
Robust speech recognition via large-scale weak supervision A Radford, JW Kim, T Xu, G Brockman, C McLeavey, I Sutskever International conference on machine learning, 28492-28518, 2023 | 3836 | 2023 |
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 | 3827 | 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 | 2733 | 2020 |
Generating long sequences with sparse transformers R Child, S Gray, A Radford, I Sutskever arXiv preprint arXiv:1904.10509, 2019 | 2046 | 2019 |
Generative pretraining from pixels M Chen, A Radford, R Child, J Wu, H Jun, D Luan, I Sutskever International conference on machine learning, 1691-1703, 2020 | 1875 | 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 | 1868 | 2020 |
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 | 1534 | 2019 |
Openai baselines P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ... | 1105 | 2017 |
Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 959 | 2018 |
Jukebox: A generative model for music P Dhariwal, H Jun, C Payne, JW Kim, A Radford, I Sutskever arXiv preprint arXiv:2005.00341, 2020 | 900 | 2020 |
Release strategies and the social impacts of language models I Solaiman, M Brundage, J Clark, A Askell, A Herbert-Voss, J Wu, ... arXiv preprint arXiv:1908.09203, 2019 | 620 | 2019 |