| Exploiting cloze questions for few shot text classification and natural language inference T Schick, H Schütze arXiv preprint arXiv:2001.07676, 2020 | 1045 | 2020 |
| 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 | 706 | 2022 |
| 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 | 658 | 2020 |
| 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 | 439 | 2022 |
| Toolformer: Language models can teach themselves to use tools T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, L Zettlemoyer, ... arXiv preprint arXiv:2302.04761, 2023 | 345 | 2023 |
| 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 | 187* | 2022 |
| 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 | 182 | 2021 |
| Augmented language models: a survey G Mialon, R Dessì, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... arXiv preprint arXiv:2302.07842, 2023 | 165 | 2023 |
| 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 | 142 | 2020 |
| Generating datasets with pretrained language models T Schick, H Schütze arXiv preprint arXiv:2104.07540, 2021 | 128 | 2021 |
| 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 | 98 | 2020 |
| Few-shot text generation with natural language instructions T Schick, H Schütze Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 78 | 2021 |
| Few-shot text generation with pattern-exploiting training T Schick, H Schütze arXiv preprint arXiv:2012.11926, 2020 | 75 | 2020 |
| Unnatural instructions: Tuning language models with (almost) no human labor O Honovich, T Scialom, O Levy, T Schick arXiv preprint arXiv:2212.09689, 2022 | 70 | 2022 |
| 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 | 54 | 2022 |
| Attentive mimicking: Better word embeddings by attending to informative contexts T Schick, H Schütze arXiv preprint arXiv:1904.01617, 2019 | 46 | 2019 |
| BERTRAM: Improved word embeddings have big impact on contextualized model performance T Schick, H Schütze arXiv preprint arXiv:1910.07181, 2019 | 39 | 2019 |
| 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 | 37 | 2022 |
| 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 | 30 | 2022 |
| Learning semantic representations for novel words: Leveraging both form and context T Schick, H Schütze Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 6965-6973, 2019 | 30 | 2019 |
Hinrich SchützeUniversity of MunichVerified email at cis.lmu.de
