| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1768 | 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 | 1352 | 2022 |
| 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 | 841 | 2022 |
| 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 | 754 | 2020 |
| 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 | 704 | 2019 |
| Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks J Phang, T Févry, SR Bowman arXiv preprint arXiv:1811.01088, 2018 | 495 | 2018 |
| 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 | 329 | 2021 |
| Tool learning with foundation models Y Qin, S Hu, Y Lin, W Chen, N Ding, G Cui, Z Zeng, X Zhou, Y Huang, ... ACM Computing Surveys 57 (4), 1-40, 2024 | 314 | 2024 |
| 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 | 299 | 2020 |
| Gpt-4o system card A Hurst, A Lerer, AP Goucher, A Perelman, A Ramesh, A Clark, AJ Ostrow, ... arXiv preprint arXiv:2410.21276, 2024 | 213 | 2024 |
| 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 | 200 | 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 | 191 | 2021 |
| 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 10, 8-9, 2021 | 165 | 2021 |
| Do attention heads in BERT track syntactic dependencies? PM Htut, J Phang, S Bordia, SR Bowman arXiv preprint arXiv:1911.12246, 2019 | 157 | 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 | 139 | 2019 |
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| 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 | 119 | 2022 |
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| Unsupervised sentence compression using denoising auto-encoders T Fevry, J Phang arXiv preprint arXiv:1809.02669, 2018 | 81 | 2018 |
| 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 | 77 | 2020 |
Samuel R. BowmanAnthropic and NYUPotvrđena adresa e-pošte na anthropic.com