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Tom B Brown
Tom B Brown
Anthropic
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Title
Cited by
Cited by
Year
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
26611*2020
Deep reinforcement learning from human preferences
PF Christiano, J Leike, T Brown, M Martic, S Legg, D Amodei
Advances in neural information processing systems 30, 2017
18412017
Adversarial patch
TB Brown, D ManÚ, A Roy, M Abadi, J Gilmer
arXiv preprint arXiv:1712.09665, 2017
11662017
Extracting training data from large language models
N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ...
30th USENIX Security Symposium (USENIX Security 21), 2633-2650, 2021
10702021
Scaling laws for neural language models
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ...
arXiv preprint arXiv:2001.08361, 2020
9252020
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
7312019
Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess
arXiv preprint arXiv:2001.08361 2, 2020
6692020
Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess
arXiv preprint arXiv:2001.08361 1 (2), 4, 2020
5512020
Technical report on the cleverhans v2. 1.0 adversarial examples library
N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ...
arXiv preprint arXiv:1610.00768, 2016
518*2016
Constitutional ai: Harmlessness from ai feedback
Y Bai, S Kadavath, S Kundu, A Askell, J Kernion, A Jones, A Chen, ...
arXiv preprint arXiv:2212.08073, 2022
4572022
cleverhans v2. 0.0: an adversarial machine learning library
N Papernot, I Goodfellow, R Sheatsley, R Feinman, P McDaniel
arXiv preprint arXiv:1610.00768 10, 2016
3152016
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
2122020
Training a helpful and harmless assistant with reinforcement learning from human feedback
Y Bai, A Jones, K Ndousse, A Askell, A Chen, N DasSarma, D Drain, ...
arXiv preprint arXiv:2204.05862, 2022
2042022
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
D Ganguli, L Lovitt, J Kernion, A Askell, Y Bai, S Kadavath, B Mann, ...
arXiv preprint arXiv:2209.07858, 2022
1622022
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
1432005
In-context learning and induction heads
C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ...
arXiv preprint arXiv:2209.11895, 2022
1392022
Predictability and surprise in large generative models
D Ganguli, D Hernandez, L Lovitt, A Askell, Y Bai, A Chen, T Conerly, ...
Proceedings of the 2022 ACM Conference on Fairness, Accountability, andá…, 2022
1342022
Testing robustness against unforeseen adversaries
M Kaufmann, D Kang, Y Sun, S Basart, X Yin, M Mazeika, A Arora, ...
arXiv preprint arXiv:1908.08016, 2019
1322019
Is generator conditioning causally related to GAN performance?
A Odena, J Buckman, C Olsson, T Brown, C Olah, C Raffel, I Goodfellow
International conference on machine learning, 3849-3858, 2018
1322018
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
1322005
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Articles 1–20