Sebastian Gehrman
Sebastian Gehrman
Head of NLP, CTO Office, Bloomberg LP
Verified email at - Homepage
Cited by
Cited by
PaLM: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
arXiv preprint arXiv:2204.02311, 2022
Bottom-up abstractive summarization
S Gehrmann, Y Deng, AM Rush
EMNLP 2018, 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
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
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
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
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
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
GLTR: Statistical detection and visualization of generated text
S Gehrmann*, H Strobelt*, AM Rush
ACL Demo 2019, 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
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
exBERT: A visual analysis tool to explore learned representations in transformers models
B Hoover, H Strobelt, S Gehrmann
EMNLP Demo 2019, 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
The GEM benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
GEM Workshop at ACL 2021, 2021
End-to-end content and plan selection for data-to-text generation
S Gehrmann, FZ Dai, H Elder, AM Rush
INLG 2018, 2018
Encoder-agnostic adaptation for conditional language generation
ZM Ziegler, L Melas-Kyriazi, S Gehrmann, AM Rush
arXiv preprint arXiv:1908.06938, 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
Interpretability and analysis in neural NLP
Y Belinkov, S Gehrmann, E Pavlick
Proceedings of the 58th annual meeting of the association for computational …, 2020
LSTM networks can perform dynamic counting
M Suzgun, S Gehrmann, Y Belinkov, SM Shieber
arXiv preprint arXiv:1906.03648, 2019
Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
S Gehrmann, E Clark, T Sellam
JAIR, 2022
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