| 2024 | EMNLP | LongForm: Effective Instruction Tuning with Reverse Instructions. | Abdullatif Kksal, Timo Schick, Anna Korhonen, Hinrich Schtze |
| 2024 | ICLR | Self-Alignment with Instruction Backtranslation. | Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, Mike Lewis |
| 2023 | ACL | Task-aware Retrieval with Instructions. | Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih |
| 2023 | ACL | Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. | Or Honovich, Thomas Scialom, Omer Levy, Timo Schick |
| 2023 | EMNLP | MEAL: Stable and Active Learning for Few-Shot Prompting. | Abdullatif Kksal, Timo Schick, Hinrich Schtze |
| 2023 | EMNLP | Active Learning Principles for In-Context Learning with Large Language Models. | Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu |
| 2023 | ICLR | PEER: A Collaborative Language Model. | Timo Schick, Jane A. Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel |
| 2022 | ACL | CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment. | Ltfi Kerem Senel, Timo Schick, Hinrich Schtze |
| 2022 | EMNLP | Leveraging QA Datasets to Improve Generative Data Augmentation. | Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang |
| 2021 | EACL | Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. | Timo Schick, Hinrich Schtze |
| 2021 | EMNLP | Few-Shot Text Generation with Natural Language Instructions. | Timo Schick, Hinrich Schtze |
| 2021 | EMNLP | Generating Datasets with Pretrained Language Models. | Timo Schick, Hinrich Schtze |
| 2021 | NAACL | It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. | Timo Schick, Hinrich Schtze |
| 2020 | AAAI | Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking. | Timo Schick, Hinrich Schtze |
| 2020 | ACL | BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. | Timo Schick, Hinrich Schtze |
| 2020 | COLING | Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. | Timo Schick, Helmut Schmid, Hinrich Schtze |
| 2019 | AAAI | Learning Semantic Representations for Novel Words: Leveraging Both Form and Context. | Timo Schick, Hinrich Schtze |
| 2019 | NAACL | Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts. | Timo Schick, Hinrich Schtze |