| 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 | 40542 | 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 | 1929 | 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 | 1572 | 2019 |
| Scaling laws for autoregressive generative modeling T Henighan, J Kaplan, M Katz, M Chen, C Hesse, J Jackson, H Jun, ... arXiv preprint arXiv:2010.14701, 2020 | 392 | 2020 |
| Using Crash Hoare logic for certifying the FSCQ file system H Chen, D Ziegler, T Chajed, A Chlipala, MF Kaashoek, N Zeldovich Proceedings of the 25th Symposium on Operating Systems Principles, 18-37, 2015 | 334 | 2015 |
| 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 | 281 | 2021 |
| Language models are few-shot learners B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ... arXiv preprint arXiv:2005.14165 1, 3, 2020 | 279 | 2020 |
| Language Models are Few-Shot Learners. 2020. doi: 10.48550 TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... arxiv 1, 2005 | 267 | 2005 |
| Language models are few-shot learners (arXiv: 2005.14165). arXiv TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... | 179 | 2005 |
| Sleeper agents: Training deceptive llms that persist through safety training E Hubinger, C Denison, J Mu, M Lambert, M Tong, M MacDiarmid, ... arXiv preprint arXiv:2401.05566, 2024 | 138 | 2024 |
| 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 |
| & Amodei, D.(2020) TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... Language models are few-shot learners, 1877-1901, 2005 | 77 | 2005 |
| 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 | 63 | 1909 |
| Adversarial training for high-stakes reliability D Ziegler, S Nix, L Chan, T Bauman, P Schmidt-Nielsen, T Lin, A Scherlis, ... Advances in neural information processing systems 35, 9274-9286, 2022 | 55 | 2022 |
| 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 | 51 | 2019 |
| Specifying crash safety for storage systems H Chen, D Ziegler, A Chlipala, MF Kaashoek, E Kohler, N Zeldovich 15th Workshop on Hot Topics in Operating Systems (HotOS XV), 2015 | 26 | 2015 |
| Learning to summarize from human feedback, 2022 N Stiennon, L Ouyang, J Wu, DM Ziegler, R Lowe, C Voss, A Radford, ... URL https://arxiv. org/abs, 2009 | 25 | 2009 |
| Certifying a file system using crash hoare logic: correctness in the presence of crashes T Chajed, H Chen, A Chlipala, MF Kaashoek, N Zeldovich, D Ziegler Communications of the ACM 60 (4), 75-84, 2017 | 17 | 2017 |
| Recursively summarizing books with human feedback, 2021 J Wu, L Ouyang, DM Ziegler, N Stiennon, R Lowe, J Leike, P Christiano URL https://arxiv. org/abs/2109.10862, 0 | 11 | |
| Arboral satisfaction: Recognition and LP approximation ED Demaine, V Ganesan, V Kontsevoi, Q Liu, Q Liu, F Ma, O Nachum, ... Information Processing Letters 127, 1-5, 2017 | 1 | 2017 |
Jeffrey WuAnthropic AI, OpenAIE-mail megerősítve itt: anthropic.com