| PaLM: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311, 2022 | 846 | 2022 |
| Bottom-up abstractive summarization S Gehrmann, Y Deng, AM Rush EMNLP 2018, 2018 | 677 | 2018 |
| LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks H Strobelt*, S Gehrmann*, H Pfister, AM Rush IEEE transactions on visualization and computer graphics 24 (1), 667-676, 2017 | 442 | 2017 |
| Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives S Gehrmann, F Dernoncourt, Y Li, ET Carlson, JT Wu, J Welt, J Foote Jr, ... PloS one 13 (2), e0192360, 2018 | 219* | 2018 |
| Seq2Seq-Vis: A visual debugging tool for sequence-to-sequence models H Strobelt*, S Gehrmann*, M Behrisch, A Perer, H Pfister, AM Rush IEEE transactions on visualization and computer graphics 25 (1), 353-363, 2018 | 201 | 2018 |
| ToTTo: A controlled table-to-text generation dataset AP Parikh, X Wang, S Gehrmann, M Faruqui, B Dhingra, D Yang, D Das EMNLP 2020, 2020 | 200 | 2020 |
| Investigating gender bias in language models using causal mediation analysis J Vig*, S Gehrmann*, Y Belinkov*, S Qian, D Nevo, Y Singer, S Shieber NeurIPS 2021 33, 12388-12401, 2020 | 188* | 2020 |
| Bloom: A 176b-parameter open-access multilingual language model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 179 | 2022 |
| GLTR: Statistical detection and visualization of generated text S Gehrmann*, H Strobelt*, AM Rush ACL Demo 2019, 2019 | 177 | 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 | 169 | 2022 |
| Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations P Das, T Sercu, K Wadhawan, I Padhi, S Gehrmann, F Cipcigan, ... Nature Biomedical Engineering 5 (6), 613-623, 2021 | 140* | 2021 |
| exBERT: A visual analysis tool to explore learned representations in transformers models B Hoover, H Strobelt, S Gehrmann EMNLP Demo 2019, 2019 | 125 | 2019 |
| The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models I Tenney, J Wexler, J Bastings, T Bolukbasi, A Coenen, S Gehrmann, ... ACL Demo 2020, 2020 | 108 | 2020 |
| The GEM benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... GEM Workshop at ACL 2021, 2021 | 83 | 2021 |
| End-to-end content and plan selection for data-to-text generation S Gehrmann, FZ Dai, H Elder, AM Rush INLG 2018, 2018 | 74 | 2018 |
| Encoder-agnostic adaptation for conditional language generation ZM Ziegler, L Melas-Kyriazi, S Gehrmann, AM Rush arXiv preprint arXiv:1908.06938, 2019 | 49 | 2019 |
| Visual interaction with deep learning models through collaborative semantic inference S Gehrmann, H Strobelt, R Krüger, H Pfister, AM Rush IEEE transactions on visualization and computer graphics 26 (1), 884-894, 2019 | 48 | 2019 |
| Interpretability and analysis in neural NLP Y Belinkov, S Gehrmann, E Pavlick Proceedings of the 58th annual meeting of the association for computational …, 2020 | 46 | 2020 |
| LSTM networks can perform dynamic counting M Suzgun, S Gehrmann, Y Belinkov, SM Shieber arXiv preprint arXiv:1906.03648, 2019 | 45 | 2019 |
| Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text S Gehrmann, E Clark, T Sellam JAIR, 2022 | 37 | 2022 |
Hendrik StrobeltIBM Research / MIT-IBM Watson AI LabVerified email at strobelt.com
