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Amanda Askell
Amanda Askell
Anthropic
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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
34743*2020
Learning transferable visual models from natural language supervision
A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ...
International conference on machine learning, 8748-8763, 2021
184672021
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
75182022
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
9342022
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
8592022
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
7462022
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
4462019
Toward trustworthy AI development: mechanisms for supporting verifiable claims
M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ...
arXiv preprint arXiv:2004.07213, 2020
3522020
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
2892022
A general language assistant as a laboratory for alignment
A Askell, Y Bai, A Chen, D Drain, D Ganguli, T Henighan, A Jones, ...
arXiv preprint arXiv:2112.00861, 2021
2762021
In-context learning and induction heads
C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ...
arXiv preprint arXiv:2209.11895, 2022
2252022
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, and …, 2022
2192022
Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Language models (mostly) know what they know
S Kadavath, T Conerly, A Askell, T Henighan
arXiv preprint arXiv:2207.05221, 2022
1952022
A mathematical framework for transformer circuits
N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ...
Transformer Circuits Thread 1 (1), 12, 2021
1922021
Training language models to follow instructions with human feedback, 2022
L Ouyang, J Wu, X Jiang, D Almeida, CL Wainwright, P Mishkin, C Zhang, ...
URL https://arxiv. org/abs/2203.02155 13, 1, 2022
1802022
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
1712005
Discovering language model behaviors with model-written evaluations
E Perez, S Ringer, K Lukošiūtė, K Nguyen, E Chen, S Heiner, C Pettit, ...
arXiv preprint arXiv:2212.09251, 2022
1612022
Dawn Drain
N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ...
Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson …, 2021
1402021
Dawn Drain
C Olsson, N Elhage, NJ Neel Nanda, N DasSarma, T Henighan, B Mann, ...
Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy …, 2022
1282022
The capacity for moral self-correction in large language models
D Ganguli, A Askell, N Schiefer, TI Liao, K Lukošiūtė, A Chen, A Goldie, ...
arXiv preprint arXiv:2302.07459, 2023
1122023
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