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Timo Schick
Timo Schick
NLP Researcher
Bestätigte E-Mail-Adresse bei fb.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Exploiting cloze questions for few shot text classification and natural language inference
T Schick, H Schütze
arXiv preprint arXiv:2001.07676, 2020
11982020
Bloom: A 176b-parameter open-access multilingual language model
BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ...
arXiv preprint arXiv:2211.05100, 2022
9692022
It's not just size that matters: Small language models are also few-shot learners
T Schick, H Schütze
arXiv preprint arXiv:2009.07118, 2020
7282020
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
6032022
Toolformer: Language models can teach themselves to use tools
T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, E Hambro, ...
Advances in Neural Information Processing Systems 36, 2024
5952024
Atlas: Few-shot learning with retrieval augmented language models
G Izacard, P Lewis, M Lomeli, L Hosseini, F Petroni, T Schick, ...
arXiv preprint arXiv 2208, 2022
280*2022
Augmented language models: a survey
G Mialon, R Dessì, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ...
arXiv preprint arXiv:2302.07842, 2023
2532023
Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
T Schick, S Udupa, H Schütze
Transactions of the Association for Computational Linguistics 9, 1408-1424, 2021
2202021
Automatically identifying words that can serve as labels for few-shot text classification
T Schick, H Schmid, H Schütze
arXiv preprint arXiv:2010.13641, 2020
1642020
Generating datasets with pretrained language models
T Schick, H Schütze
arXiv preprint arXiv:2104.07540, 2021
1482021
Unnatural instructions: Tuning language models with (almost) no human labor
O Honovich, T Scialom, O Levy, T Schick
arXiv preprint arXiv:2212.09689, 2022
1362022
Rare words: A major problem for contextualized embeddings and how to fix it by attentive mimicking
T Schick, H Schütze
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8766-8774, 2020
1022020
Few-shot text generation with natural language instructions
T Schick, H Schütze
Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021
922021
Few-shot text generation with pattern-exploiting training
T Schick, H Schütze
arXiv preprint arXiv:2012.11926, 2020
852020
Peer: A collaborative language model
T Schick, J Dwivedi-Yu, Z Jiang, F Petroni, P Lewis, G Izacard, Q You, ...
arXiv preprint arXiv:2208.11663, 2022
692022
Self-alignment with instruction backtranslation
X Li, P Yu, C Zhou, T Schick, L Zettlemoyer, O Levy, J Weston, M Lewis
arXiv preprint arXiv:2308.06259, 2023
462023
Attentive mimicking: Better word embeddings by attending to informative contexts
T Schick, H Schütze
arXiv preprint arXiv:1904.01617, 2019
462019
True few-shot learning with prompts—a real-world perspective
T Schick, H Schütze
Transactions of the Association for Computational Linguistics 10, 716-731, 2022
432022
BERTRAM: Improved word embeddings have big impact on contextualized model performance
T Schick, H Schütze
arXiv preprint arXiv:1910.07181, 2019
412019
Task-aware retrieval with instructions
A Asai, T Schick, P Lewis, X Chen, G Izacard, S Riedel, H Hajishirzi, ...
arXiv preprint arXiv:2211.09260, 2022
392022
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