| 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 | 41278 | 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 | 27045* | 2019 |
| Training language models to follow instructions with human feedback L Ouyang, J Wu, X Jiang, D Almeida, C Wainwright, P Mishkin, C Zhang, ... Advances in neural information processing systems 35, 27730-27744, 2022 | 12477 | 2022 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 10494* | 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 | 3040 | 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 | 1973 | 2020 |
| 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 | 1888 | 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 | 1579 | 2019 |
| Webgpt: Browser-assisted question-answering with human feedback R Nakano, J Hilton, S Balaji, J Wu, L Ouyang, C Kim, C Hesse, S Jain, ... arXiv preprint arXiv:2112.09332, 2021 | 1186 | 2021 |
| 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 | 697 | 2019 |
| Recursively summarizing books with human feedback J Wu, L Ouyang, DM Ziegler, N Stiennon, R Lowe, J Leike, P Christiano arXiv preprint arXiv:2109.10862, 2021 | 293 | 2021 |
| Language models can explain neurons in language models S Bills, N Cammarata, D Mossing, H Tillman, L Gao, G Goh, I Sutskever, ... URL https://openaipublic. blob. core. windows. net/neuron-explainer/paper …, 2023 | 245 | 2023 |
| Self-critiquing models for assisting human evaluators W Saunders, C Yeh, J Wu, S Bills, L Ouyang, J Ward, J Leike arXiv preprint arXiv:2206.05802, 2022 | 238 | 2022 |
| Weak-to-strong generalization: Eliciting strong capabilities with weak supervision C Burns, P Izmailov, JH Kirchner, B Baker, L Gao, L Aschenbrenner, ... arXiv preprint arXiv:2312.09390, 2023 | 223 | 2023 |
| 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 | 119 | 2005 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 109 | 2024 |
| Scaling and evaluating sparse autoencoders L Gao, TD la Tour, H Tillman, G Goh, R Troll, A Radford, I Sutskever, ... arXiv preprint arXiv:2406.04093, 2024 | 96 | 2024 |
| Fine-tuning language models from human preferences, 2020 DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... URL https://arxiv. org/abs, 14, 1909 | 64 | 1909 |
| Fine-tuning language models from human preferences. arXiv DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... arXiv preprint arXiv:1909.08593 10, 2019 | 52 | 2019 |
| Fmb: a functional manipulation benchmark for generalizable robotic learning J Luo, C Xu, F Liu, L Tan, Z Lin, J Wu, P Abbeel, S Levine The International Journal of Robotics Research, 02783649241276017, 2023 | 29 | 2023 |
