StereoSet: Measuring stereotypical bias in pretrained language models M Nadeem, A Bethke, S Reddy arXiv preprint arXiv:2004.09456, 2020 | 694 | 2020 |
Identifying depression on Twitter M Nadeem arXiv preprint arXiv:1607.07384, 2016 | 169 | 2016 |
The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 134 | 2021 |
Fakta: An automatic end-to-end fact checking system M Nadeem, W Fang, B Xu, M Mohtarami, J Glass Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 53 | 2019 |
Neural multi-task learning for stance prediction W Fang, M Nadeem, M Mohtarami, J Glass Proceedings of the second workshop on fact extraction and verification …, 2019 | 30 | 2019 |
A systematic characterization of sampling algorithms for open-ended language generation M Nadeem, T He, K Cho, J Glass arXiv preprint arXiv:2009.07243, 2020 | 27 | 2020 |
Mosaicbert: How to train bert with a lunch money budget J Portes, AR Trott, S Havens, D King, A Venigalla, M Nadeem, N Sardana, ... Workshop on Efficient Systems for Foundation Models@ ICML2023, 2023 | 5 | 2023 |
Neural Educational Recommendation Engine (NERE) M Nadeem, D Stansbury, S Mooney 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 343-348, 2018 | 3 | 2018 |
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining J Portes, A Trott, S Havens, D King, A Venigalla, M Nadeem, N Sardana, ... Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
On factuality in neural language models M Nadeem Massachusetts Institute of Technology, 2021 | | 2021 |