| GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding A Wang, A Singh, J Michael, F Hill, O Levy, SR Bowman Proceedings of ICLR, 2019 | 5543 | 2019 |
| A large annotated corpus for learning natural language inference SR Bowman, G Angeli, C Potts, CD Manning Proceedings of EMNLP, 2015 | 4147 | 2015 |
| A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference A Williams, N Nangia, SR Bowman Proceedings of NAACL-HLT, 2018 | 3656 | 2018 |
| Generating sentences from a continuous space SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio Proceedings of CoNLL, 2016 | 2542 | 2016 |
| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... Proceedings of NeurIPS, 2019 | 1620 | 2019 |
| Annotation artifacts in natural language inference data S Gururangan, S Swayamdipta, O Levy, R Schwartz, SR Bowman, ... Proceedings of NAACL, 2018 | 1059 | 2018 |
| XNLI: Evaluating Cross-lingual Sentence Representations A Conneau, G Lample, R Rinott, A Williams, SR Bowman, H Schwenk, ... Proceedings of EMNLP, 2018 | 1042 | 2018 |
| Neural network acceptability judgments A Warstadt, A Singh, SR Bowman TACL 7, 625-641, 2019 | 1008 | 2019 |
| What do you learn from context? Probing for sentence structure in contextualized word representations I Tenney, P Xia, B Chen, A Wang, A Poliak, RT McCoy, N Kim, ... Proceedings of ICLR, 2019 | 748 | 2019 |
| On Measuring Social Biases in Sentence Encoders C May, A Wang, S Bordia, SR Bowman, R Rudinger Proceedings of NAACL-HLT, 2019 | 450 | 2019 |
| 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 | 444 | 2022 |
| Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks J Phang, T Févry, SR Bowman arXiv preprint 1811.01088, 2018 | 411 | 2018 |
| A Fast Unified Model for Parsing and Sentence Understanding SR Bowman, J Gauthier, A Rastogi, R Gupta, CD Manning, C Potts Proceedings of ACL, 2016 | 400 | 2016 |
| Universal Dependencies 2.2 J Nivre, M Abrams, Ž Agić, L Ahrenberg, L Antonsen, MJ Aranzabe, ... | 328* | 2018 |
| CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models N Nangia, C Vania, R Bhalerao, SR Bowman Proceedings of EMNLP, 2020 | 302 | 2020 |
| A Gold Standard Dependency Corpus for English N Silveira, T Dozat, MC de Marneffe, SR Bowman, M Connor, J Bauer, ... Proceedings of LREC, 2014 | 302 | 2014 |
| Constitutional AI: Harmlessness from AI feedback Y Bai, S Kadavath, S Kundu, A Askell, J Kernion, A Jones, A Chen, ... arXiv preprint arXiv:2212.08073, 2022 | 281 | 2022 |
| BLiMP: A benchmark of linguistic minimal pairs for english A Warstadt, A Parrish, H Liu, A Mohananey, W Peng, SF Wang, ... TACL, 2020 | 263 | 2020 |
| Identifying and Reducing Gender Bias in Word-Level Language Models S Bordia, SR Bowman Proceedings of the NAACL-HLT Student Research Workshop, 2019 | 247 | 2019 |
| 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, ... Proceedings of ACL, 2020 | 243 | 2020 |
