Suivre
Daniel M. Ziegler
Daniel M. Ziegler
Redwood Research
Adresse e-mail validée de rdwrs.com - Page d'accueil
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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
241592020
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
10032020
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
8452019
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
3162015
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
2312020
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
1832021
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
1442005
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
1372005
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
702005
Language models are few-shot learners
B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ...
arXiv preprint arXiv:2005.14165, 2020
472020
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
352022
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
242015
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
162017
Language models are few-shot learners.[Cs]
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
Proceedings of 2020 Neural Information Processing Systems, 2020
132020
Learning to summarize from human feedback, 2020
N Stiennon, L Ouyang, J Wu, DM Ziegler, R Lowe, C Voss, A Radford, ...
URL https://arxiv. org/abs, 2009
82009
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
7
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
62024
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
12017
Compiling Gallina to Go for the FSCQ file system
D Ziegler
Massachusetts Institute of Technology, 2017
12017
2. A. Bordes, Y. Boureau, and J. Weston. Learning end-to-end goal-oriented dialog. In 5th
GS Shyam, A Askell, S Agarwal, A Herbert-Voss, G Krueger, T Henighan, ...
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