Transition-Based Generation from Abstract Meaning Representations. - Timo Schick (CoRR, 2017)

Learning Semantic Representations for Novel Words: Leveraging Both Form and Context. - Timo Schick; Hinrich Schütze (AAAI, 2019)

Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts. - Timo Schick; Hinrich Schütze (NAACL-HLT (1), 2019)

Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking. - Timo Schick; Hinrich Schütze (AAAI, 2020)

BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. - Timo Schick; Hinrich Schütze (ACL, 2020)

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. - Timo Schick; Helmut Schmid; Hinrich Schütze (COLING, 2020)

Few-Shot Text Generation with Pattern-Exploiting Training. - Timo Schick; Hinrich Schütze (CoRR, 2020)

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. - Timo Schick; Hinrich Schütze (EACL, 2021)

Few-Shot Text Generation with Natural Language Instructions. - Timo Schick; Hinrich Schütze (EMNLP (1), 2021)

Generating Datasets with Pretrained Language Models. - Timo Schick; Hinrich Schütze (EMNLP (1), 2021)

It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. - Timo Schick; Hinrich Schütze (NAACL-HLT, 2021)

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP. - Timo Schick; Sahana Udupa; Hinrich Schütze (Trans. Assoc. Comput. Linguistics, 2021)

CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment. - Lütfi Kerem Senel; Timo Schick; Hinrich Schütze (ACL (2), 2022)

Leveraging QA Datasets to Improve Generative Data Augmentation. - Dheeraj Mekala; Tu Vu; Timo Schick; Jingbo Shang (EMNLP, 2022)

Improving Wikipedia Verifiability with AI. - Fabio Petroni; Samuel Broscheit; Aleksandra Piktus; Patrick Lewis 001; Gautier Izacard; Lucas Hosseini; Jane Dwivedi-Yu; Maria Lomeli; Timo Schick; Pierre-Emmanuel Mazaré; Armand Joulin; Edouard Grave; et al. (CoRR, 2022)

Few-shot Learning with Retrieval Augmented Language Models. - Gautier Izacard; Patrick Lewis 001; Maria Lomeli; Lucas Hosseini; Fabio Petroni; Timo Schick; Jane Dwivedi-Yu; Armand Joulin; Sebastian Riedel 001; Edouard Grave (CoRR, 2022)

EditEval: An Instruction-Based Benchmark for Text Improvements. - Jane Dwivedi-Yu; Timo Schick; Zhengbao Jiang; Maria Lomeli; Patrick Lewis 001; Gautier Izacard; Edouard Grave; Sebastian Riedel 001; Fabio Petroni (CoRR, 2022)

True Few-Shot Learning With Prompts - A Real-World Perspective. - Timo Schick; Hinrich Schütze (Trans. Assoc. Comput. Linguistics, 2022)

Few-Shot Learning mit Sprachmodellen. - Timo Schick (Ausgezeichnete Informatikdissertationen, 2022)

Task-aware Retrieval with Instructions. - Akari Asai; Timo Schick; Patrick Lewis 001; Xilun Chen 002; Gautier Izacard; Sebastian Riedel 001; Hannaneh Hajishirzi; Wen-tau Yih (ACL (Findings), 2023)

Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. - Or Honovich; Thomas Scialom; Omer Levy; Timo Schick (ACL (1), 2023)

MEAL: Stable and Active Learning for Few-Shot Prompting. - Abdullatif Köksal; Timo Schick; Hinrich Schütze (EMNLP (Findings), 2023)

Active Learning Principles for In-Context Learning with Large Language Models. - Katerina Margatina; Timo Schick; Nikolaos Aletras; Jane Dwivedi-Yu (EMNLP (Findings), 2023)

PEER: A Collaborative Language Model. - Timo Schick; Jane A. Yu; Zhengbao Jiang; Fabio Petroni; Patrick Lewis 001; Gautier Izacard; Qingfei You; Christoforos Nalmpantis; Edouard Grave; Sebastian Riedel 001 (ICLR, 2023)

Toolformer: Language Models Can Teach Themselves to Use Tools. - Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Eric Hambro; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom (NeurIPS, 2023)

Semantic-Oriented Unlabeled Priming for Large-Scale Language Models. - Yanchen Liu; Timo Schick; Hinrich Schütze (SustaiNLP, 2023)

Toolformer: Language Models Can Teach Themselves to Use Tools. - Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom (CoRR, 2023)

LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction. - Abdullatif Köksal; Timo Schick; Anna Korhonen; Hinrich Schütze (CoRR, 2023)

Evaluation of Faithfulness Using the Longest Supported Subsequence. - Anirudh Mittal; Timo Schick; Mikel Artetxe; Jane Dwivedi-Yu (CoRR, 2023)

Atlas: Few-shot Learning with Retrieval Augmented Language Models. - Gautier Izacard; Patrick Lewis 001; Maria Lomeli; Lucas Hosseini; Fabio Petroni; Timo Schick; Jane Dwivedi-Yu; Armand Joulin; Sebastian Riedel 001; Edouard Grave (J. Mach. Learn. Res., 2023)

Augmented Language Models: a Survey. - Grégoire Mialon; Roberto Dessì; Maria Lomeli; Christoforos Nalmpantis; Ramakanth Pasunuru; Roberta Raileanu; Baptiste Rozière; Timo Schick; Jane Dwivedi-Yu; Asli Celikyilmaz; Edouard Grave; Yann LeCun; et al. (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Improving Wikipedia verifiability with AI. - Fabio Petroni; Samuel Broscheit; Aleksandra Piktus; Patrick Lewis 001; Gautier Izacard; Lucas Hosseini; Jane Dwivedi-Yu; Maria Lomeli; Timo Schick; Michele Bevilacqua; Pierre-Emmanuel Mazaré; Armand Joulin; et al. (Nat. Mac. Intell., 2023)

LongForm: Effective Instruction Tuning with Reverse Instructions. - Abdullatif Köksal; Timo Schick; Anna Korhonen; Hinrich Schütze (EMNLP (Findings), 2024)

Self-Alignment with Instruction Backtranslation. - Xian Li 003; Ping Yu; Chunting Zhou; Timo Schick; Omer Levy; Luke Zettlemoyer; Jason Weston; Mike Lewis (ICLR, 2024)

FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations. - Jane Dwivedi-Yu; Raaz Dwivedi; Timo Schick (CoRR, 2024)