| Language models are few-shot learners TB Brown arXiv preprint arXiv:2005.14165, 2020 | 33100 | 2020 |
| Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford …, 2020 | 8187 | 2020 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 4240 | 2023 |
| GPT-4 technical report R OpenAI ArXiv 2303, 08774, 2023 | 1114 | 2023 |
| Adding gradient noise improves learning for very deep networks A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens International Conference on Learning Representations Workshop (ICLR Workshop …, 2015 | 620 | 2015 |
| Efficient non-parametric estimation of multiple embeddings per word in vector space A Neelakantan, J Shankar, A Passos, A McCallum Conference on Empirical Methods in Natural Language Processing, 2014, 2015 | 615 | 2015 |
| Text and code embeddings by contrastive pre-training A Neelakantan, T Xu, R Puri, A Radford, JM Han, J Tworek, Q Yuan, ... arXiv preprint arXiv:2201.10005, 2022 | 363 | 2022 |
| Compositional vector space models for knowledge base completion A Neelakantan, B Roth, A McCallum arXiv preprint arXiv:1504.06662, 2015 | 353 | 2015 |
| Chains of reasoning over entities, relations, and text using recurrent neural networks R Das, A Neelakantan, D Belanger, A McCallum European Chapter of the Association for Computational Linguistics (EACL), 2017., 2016 | 330 | 2016 |
| Neural programmer: Inducing latent programs with gradient descent A Neelakantan, QV Le, I Sutskever International Conference on Learning Representations (ICLR), 2016, 2015 | 295 | 2015 |
| Taskmaster-1: Toward a realistic and diverse dialog dataset B Byrne, K Krishnamoorthi, C Sankar, A Neelakantan, D Duckworth, ... arXiv preprint arXiv:1909.05358, 2019 | 222 | 2019 |
| Language Models are Few-Shot Learners. 2020. doi: 10.48550 TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... arxiv, 5-7, 2005 | 205 | 2005 |
| Learning a natural language interface with neural programmer A Neelakantan, QV Le, M Abadi, A McCallum, D Amodei International Conference on Learning Representations (ICLR), 2017., 2016 | 137 | 2016 |
| Language models are few-shot learners (arXiv: 2005.14165). arXiv TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... | 99 | 2005 |
| & Amodei, D.(2020) TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... Language models are few-shot learners, 2005 | 94 | 2005 |
| Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods A Neelakantan, MW Chang The North American Chapter of the Association for Computational Linguistics …, 2015 | 93 | 2015 |
| Language models are few-shot learners B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ... arXiv preprint arXiv:2005.14165 1, 2020 | 89 | 2020 |
| Theory and experiments on vector quantized autoencoders A Roy, A Vaswani, A Neelakantan, N Parmar arXiv preprint arXiv:1805.11063, 2018 | 89 | 2018 |
| Compositional vector space models for knowledge base inference A Neelakantan, B Roth, A McCallum 2015 aaai spring symposium series, 2015 | 83 | 2015 |
| Trading off diversity and quality in natural language generation H Zhang, D Duckworth, D Ippolito, A Neelakantan arXiv preprint arXiv:2004.10450, 2020 | 81 | 2020 |
