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Jason Phang
Jason Phang
Verified email at nyu.edu - Homepage
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Bloom: A 176b-parameter open-access multilingual language model
BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ...
arXiv preprint arXiv:2211.05100, 2022
9692022
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
6032022
Deep neural networks improve radiologists’ performance in breast cancer screening
N Wu, J Phang, J Park, Y Shen, Z Huang, M Zorin, S Jastrzębski, T Févry, ...
IEEE transactions on medical imaging 39 (4), 1184-1194, 2019
5552019
Gpt-neox-20b: An open-source autoregressive language model
S Black, S Biderman, E Hallahan, Q Anthony, L Gao, L Golding, H He, ...
arXiv preprint arXiv:2204.06745, 2022
4332022
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
J Phang, T Févry, SR Bowman
arXiv preprint arXiv:1811.01088, 2018
4312018
The pile: An 800gb dataset of diverse text for language modeling
L Gao, S Biderman, S Black, L Golding, T Hoppe, C Foster, J Phang, H He, ...
arXiv preprint arXiv:2101.00027, 2020
3562020
Intermediate-task transfer learning with pretrained models for natural language understanding: When and why does it work?
Y Pruksachatkun, J Phang, H Liu, PM Htut, X Zhang, RY Pang, C Vania, ...
arXiv preprint arXiv:2005.00628, 2020
2602020
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Y Shen, N Wu, J Phang, J Park, K Liu, S Tyagi, L Heacock, SG Kim, L Moy, ...
Medical image analysis 68, 101908, 2021
1482021
Do attention heads in BERT track syntactic dependencies?
PM Htut, J Phang, S Bordia, SR Bowman
arXiv preprint arXiv:1911.12246, 2019
1262019
Tool learning with foundation models
Y Qin, S Hu, Y Lin, W Chen, N Ding, G Cui, Z Zeng, Y Huang, C Xiao, ...
arXiv preprint arXiv:2304.08354, 2023
1252023
Investigating BERT's knowledge of language: Five analysis methods with NPIs
A Warstadt, Y Cao, I Grosu, W Peng, H Blix, Y Nie, A Alsop, S Bordia, ...
arXiv preprint arXiv:1909.02597, 2019
1232019
BBQ: A hand-built bias benchmark for question answering
A Parrish, A Chen, N Nangia, V Padmakumar, J Phang, J Thompson, ...
arXiv preprint arXiv:2110.08193, 2021
1042021
Unsupervised sentence compression using denoising auto-encoders
T Fevry, J Phang
arXiv preprint arXiv:1809.02669, 2018
722018
English intermediate-task training improves zero-shot cross-lingual transfer too
J Phang, I Calixto, PM Htut, Y Pruksachatkun, H Liu, C Vania, K Kann, ...
arXiv preprint arXiv:2005.13013, 2020
692020
Pretraining language models with human preferences
T Korbak, K Shi, A Chen, RV Bhalerao, C Buckley, J Phang, SR Bowman, ...
International Conference on Machine Learning, 17506-17533, 2023
672023
What language model to train if you have one million gpu hours?
TL Scao, T Wang, D Hesslow, L Saulnier, S Bekman, MS Bari, ...
arXiv preprint arXiv:2210.15424, 2022
592022
A framework for few-shot language model evaluation
L Gao, J Tow, S Biderman, S Black, A DiPofi, C Foster, L Golding, J Hsu, ...
Version v0. 0.1. Sept, 2021
562021
jiant 1.2: A software toolkit for research on general-purpose text understanding models
A Wang, IF Tenney, Y Pruksachatkun, K Yu, J Hula, P Xia, R Pappagari, ...
Note: http://jiant. info/Cited by: footnote 4, 2019
512019
QuALITY: Question answering with long input texts, yes!
RY Pang, A Parrish, N Joshi, N Nangia, J Phang, A Chen, V Padmakumar, ...
arXiv preprint arXiv:2112.08608, 2021
462021
jiant: A software toolkit for research on general-purpose text understanding models
Y Pruksachatkun, P Yeres, H Liu, J Phang, PM Htut, A Wang, I Tenney, ...
arXiv preprint arXiv:2003.02249, 2020
372020
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