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) 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 AnthropicEmail verificata su nyu.edu
