| 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 | 29925* | 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 | 18469* | 2019 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... Journal of Machine Learning Research 24 (240), 1-113, 2023 | 3340 | 2023 |
| Scaling laws for neural language models J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ... arXiv preprint arXiv:2001.08361, 2020 | 1783 | 2020 |
| Generating long sequences with sparse transformers R Child, S Gray, A Radford, I Sutskever arXiv preprint arXiv:1904.10509, 2019 | 1573 | 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 | 1447 | 2020 |
| Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model S Smith, M Patwary, B Norick, P LeGresley, S Rajbhandari, J Casper, ... arXiv preprint arXiv:2201.11990, 2022 | 487 | 2022 |
| Very deep vaes generalize autoregressive models and can outperform them on images R Child International Conference on Learning Representations (ICLR) 2021, Spotlight, 2020 | 274 | 2020 |
| Exploring neural transducers for end-to-end speech recognition E Battenberg, J Chen, R Child, A Coates, YGY Li, H Liu, S Satheesh, ... 2017 IEEE automatic speech recognition and understanding workshop (ASRU …, 2017 | 269* | 2017 |
| Convolutional recurrent neural networks for small-footprint keyword spotting SO Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, ... arXiv preprint arXiv:1703.05390, 2017 | 269* | 2017 |
| Language models are unsupervised multitask learners. 2019 A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever URL https://d4mucfpksywv. cloudfront. net/better-language-models/language …, 2019 | 199 | 2019 |
| 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 |
| DALL· E: Creating images from text A Ramesh, M Pavlov, G Goh, S Gray, M Chen, R Child, V Misra, P Mishkin, ... OpenAI blog. https://openai. com/blog/dall-e, 2021 | 80 | 2021 |
| Palm: Scaling language modeling with pathways. arXiv 2022 A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311 10, 2022 | 78 | 2022 |
| Language models are few-shot learners. CoRR abs/2005.14165 (2020) TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... URL: https://arxiv. org/abs/2005.14165, 2005 | 70 | 2005 |
| Active learning for speech recognition: the power of gradients J Huang, R Child, V Rao, H Liu, S Satheesh, A Coates arXiv preprint arXiv:1612.03226, 2016 | 64 | 2016 |
| Scaling laws for neural language models. arXiv 2020 J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ... arXiv preprint arXiv:2001.08361, 2001 | 55 | 2001 |
| Distribution augmentation for generative modeling H Jun, R Child, M Chen, J Schulman, A Ramesh, A Radford, I Sutskever International Conference on Machine Learning, 5006-5019, 2020 | 51 | 2020 |
| Language models are unsupervised multitask learners J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 49 | 2019 |
| Language models are few-shot learners B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ... arXiv preprint arXiv:2005.14165, 2020 | 47 | 2020 |
