Schick, Timo

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1,305

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023) · arXiv

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 1,150

It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 747

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 227

Atlas: Few-shot Learning with Retrieval Augmented Language Models

Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 213

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

Schick, T., Schmid, H., & Schütze, H. (2020) · Proceedings of the 28th International Conference on Computational Linguistics

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 184

Augmented Language Models: a Survey

Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 160

Generating Datasets with Pretrained Language Models

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 122

Few-Shot Text Generation with Natural Language Instructions

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 107

PEER: A Collaborative Language Model

Schick, T., Dwivedi-Yu, J., Jiang, Z., Petroni, F., Lewis, P., Izacard, G., You, Q., Nalmpantis, C., Grave, E., & Riedel, S. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

Task-aware Retrieval with Instructions

Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Information Retrieval and Search BehaviorTopic Modeling

Cited by 46

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · arXiv

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 1