Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1209 | 2023 |
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 | 757 | 2022 |
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 | 589 | 2019 |
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 | 521 | 2022 |
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks J Phang, T Févry, SR Bowman arXiv preprint arXiv:1811.01088, 2018 | 446 | 2018 |
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 | 438 | 2020 |
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 | 276 | 2020 |
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 | 160 | 2023 |
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 | 155 | 2021 |
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 | 137 | 2021 |
Do attention heads in BERT track syntactic dependencies? PM Htut, J Phang, S Bordia, SR Bowman arXiv preprint arXiv:1911.12246, 2019 | 133 | 2019 |
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 | 126 | 2019 |
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 | 90 | 2023 |
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 | 81 | 2022 |
Unsupervised sentence compression using denoising auto-encoders T Fevry, J Phang arXiv preprint arXiv:1809.02669, 2018 | 74 | 2018 |
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, 8, 2021 | 72 | 2021 |
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 | 71 | 2020 |
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 | 65 | 2021 |
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 | 51 | 2019 |
Investigating efficiently extending transformers for long input summarization J Phang, Y Zhao, PJ Liu arXiv preprint arXiv:2208.04347, 2022 | 42 | 2022 |