Следене
Nicholas Joseph
Nicholas Joseph
Anthropic
Потвърден имейл адрес: anthropic.com
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Позовавания
Позовавания
Година
Evaluating large language models trained on code
M Chen, J Tworek, H Jun, Q Yuan, HPDO Pinto, J Kaplan, H Edwards, ...
arXiv preprint arXiv:2107.03374, 2021
25882021
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
10312022
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
8252022
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
3192022
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
2922021
In-context learning and induction heads
C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ...
arXiv preprint arXiv:2209.11895, 2022
2452022
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
2312022
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
2192021
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
1742022
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
1452021
Towards monosemanticity: Decomposing language models with dictionary learning
T Bricken, A Templeton, J Batson, B Chen, A Jermyn, T Conerly, N Turner, ...
Transformer Circuits Thread 2, 2023
1302023
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
1202023
Language models (mostly) know what they know
S Kadavath, T Conerly, A Askell, T Henighan, D Drain, E Perez, ...
arXiv preprint arXiv:2207.05221, 2022
1112022
Towards measuring the representation of subjective global opinions in language models
E Durmus, K Nguyen, TI Liao, N Schiefer, A Askell, A Bakhtin, C Chen, ...
arXiv preprint arXiv:2306.16388, 2023
1052023
Studying large language model generalization with influence functions
R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ...
arXiv preprint arXiv:2308.03296, 2023
742023
Measuring progress on scalable oversight for large language models
SR Bowman, J Hyun, E Perez, E Chen, C Pettit, S Heiner, K Lukošiūtė, ...
arXiv preprint arXiv:2211.03540, 2022
632022
Measuring faithfulness in chain-of-thought reasoning
T Lanham, A Chen, A Radhakrishnan, B Steiner, C Denison, ...
arXiv preprint arXiv:2307.13702, 2023
582023
A mathematical framework for transformer circuits. Transformer Circuits Thread, 2021
N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ...
56
Scaling laws and interpretability of learning from repeated data
D Hernandez, T Brown, T Conerly, N DasSarma, D Drain, S El-Showk, ...
arXiv preprint arXiv:2205.10487, 2022
522022
Evaluating large language models trained on code. arXiv 2021
M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ...
arXiv preprint arXiv:2107.03374 10, 2021
402021
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