| 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 | 724 | 2022 |
| 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 | 270 | 2020 |
| Generalized inner loop meta-learning E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ... arXiv preprint arXiv:1910.01727, 2019 | 156 | 2019 |
| Do attention heads in BERT track syntactic dependencies? PM Htut, J Phang, S Bordia, SR Bowman arXiv preprint arXiv:1911.12246, 2019 | 130 | 2019 |
| 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 | 125 | 2021 |
| 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 | 125 | 2019 |
| 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 | 68 | 2020 |
| Grammar induction with neural language models: An unusual replication PM Htut, K Cho, SR Bowman arXiv preprint arXiv:1808.10000, 2018 | 56 | 2018 |
| Training a ranking function for open-domain question answering PM Htut, SR Bowman, K Cho arXiv preprint arXiv:1804.04264, 2018 | 55 | 2018 |
| 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 |
| 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 | 39 | 2020 |
| Findings of the IWSLT 2023 evaluation campaign M Agarwal, S Agarwal, A Anastasopoulos, L Bentivogli, O Bojar, C Borg, ... Association for Computational Linguistics, 2023 | 25 | 2023 |
| Comparing test sets with item response theory C Vania, PM Htut, W Huang, D Mungra, RY Pang, J Phang, H Liu, K Cho, ... arXiv preprint arXiv:2106.00840, 2021 | 23 | 2021 |
| (QA): Question Answering with Questionable Assumptions N Kim, PM Htut, SR Bowman, J Petty arXiv preprint arXiv:2212.10003, 2022 | 16 | 2022 |
| The unbearable weight of generating artificial errors for grammatical error correction PM Htut, J Tetreault arXiv preprint arXiv:1907.08889, 2019 | 13 | 2019 |
| RAMP: Retrieval and attribute-marking enhanced prompting for attribute-controlled translation G Sarti, PM Htut, X Niu, B Hsu, A Currey, G Dinu, M Nadejde arXiv preprint arXiv:2305.17131, 2023 | 5 | 2023 |
| Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory P Rodriguez, PM Htut, JP Lalor, J Sedoc Proceedings of the Third Workshop on Insights from Negative Results in NLP …, 2022 | 5 | 2022 |
| Inducing constituency trees through neural machine translation PM Htut, K Cho, SR Bowman arXiv preprint arXiv:1909.10056, 2019 | 5 | 2019 |
| ^ 2: Question Answering with Questionable Assumptions SR Bowman, PM Htut, N Kim | | 2023 |
| Analyzing and Comparing Natural Language Processing Models and Datasets PM Htut New York University, 2022 | | 2022 |
Samuel R. BowmanNYU and AnthropicVerified email at nyu.edu
