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Timo Schick
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2020 – today
- 2024
- [i28]Jane Dwivedi-Yu, Raaz Dwivedi, Timo Schick:
FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations. CoRR abs/2404.06619 (2024) - 2023
- [j4]Gautier Izacard, Patrick S. H. Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave:
Atlas: Few-shot Learning with Retrieval Augmented Language Models. J. Mach. Learn. Res. 24: 251:1-251:43 (2023) - [j3]Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick S. H. Lewis, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick, Michele Bevilacqua, Pierre-Emmanuel Mazaré, Armand Joulin, Edouard Grave, Sebastian Riedel:
Improving Wikipedia verifiability with AI. Nat. Mac. Intell. 5(10): 1142-1148 (2023) - [c18]Akari Asai, Timo Schick, Patrick S. H. Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih:
Task-aware Retrieval with Instructions. ACL (Findings) 2023: 3650-3675 - [c17]Or Honovich, Thomas Scialom, Omer Levy, Timo Schick:
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. ACL (1) 2023: 14409-14428 - [c16]Abdullatif Köksal, Timo Schick, Hinrich Schütze:
MEAL: Stable and Active Learning for Few-Shot Prompting. EMNLP (Findings) 2023: 506-517 - [c15]Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu:
Active Learning Principles for In-Context Learning with Large Language Models. EMNLP (Findings) 2023: 5011-5034 - [c14]Timo Schick, Jane A. Yu, Zhengbao Jiang, Fabio Petroni, Patrick S. H. Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel:
PEER: A Collaborative Language Model. ICLR 2023 - [c13]Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom:
Toolformer: Language Models Can Teach Themselves to Use Tools. NeurIPS 2023 - [c12]Yanchen Liu, Timo Schick, Hinrich Schütze:
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models. SustaiNLP 2023: 32-38 - [i27]Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom:
Toolformer: Language Models Can Teach Themselves to Use Tools. CoRR abs/2302.04761 (2023) - [i26]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, Thomas Scialom:
Augmented Language Models: a Survey. CoRR abs/2302.07842 (2023) - [i25]Abdullatif Köksal, Timo Schick, Anna Korhonen, Hinrich Schütze:
LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction. CoRR abs/2304.08460 (2023) - [i24]Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu:
Active Learning Principles for In-Context Learning with Large Language Models. CoRR abs/2305.14264 (2023) - [i23]Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Luke Zettlemoyer, Omer Levy, Jason Weston, Mike Lewis:
Self-Alignment with Instruction Backtranslation. CoRR abs/2308.06259 (2023) - [i22]Anirudh Mittal, Timo Schick, Mikel Artetxe, Jane Dwivedi-Yu:
Evaluation of Faithfulness Using the Longest Supported Subsequence. CoRR abs/2308.12157 (2023) - 2022
- [j2]Timo Schick, Hinrich Schütze:
True Few-Shot Learning With Prompts - A Real-World Perspective. Trans. Assoc. Comput. Linguistics 10: 716-731 (2022) - [c11]Lütfi Kerem Senel, Timo Schick, Hinrich Schütze:
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment. ACL (2) 2022: 815-824 - [c10]Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang:
Leveraging QA Datasets to Improve Generative Data Augmentation. EMNLP 2022: 9737-9750 - [p1]Timo Schick:
Few-Shot Learning mit Sprachmodellen. Ausgezeichnete Informatikdissertationen 2022: 251-260 - [i21]Yanchen Liu, Timo Schick, Hinrich Schütze:
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models. CoRR abs/2202.06133 (2022) - [i20]Lütfi Kerem Senel, Timo Schick, Hinrich Schütze:
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment. CoRR abs/2203.06228 (2022) - [i19]Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick S. H. Lewis, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick, Pierre-Emmanuel Mazaré, Armand Joulin, Edouard Grave, Sebastian Riedel:
Improving Wikipedia Verifiability with AI. CoRR abs/2207.06220 (2022) - [i18]Gautier Izacard, Patrick S. H. Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave:
Few-shot Learning with Retrieval Augmented Language Models. CoRR abs/2208.03299 (2022) - [i17]Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick S. H. Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel:
PEER: A Collaborative Language Model. CoRR abs/2208.11663 (2022) - [i16]Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick S. H. Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, Fabio Petroni:
EditEval: An Instruction-Based Benchmark for Text Improvements. CoRR abs/2209.13331 (2022) - [i15]Abdullatif Köksal, Timo Schick, Hinrich Schütze:
MEAL: Stable and Active Learning for Few-Shot Prompting. CoRR abs/2211.08358 (2022) - [i14]Akari Asai, Timo Schick, Patrick S. H. Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih:
Task-aware Retrieval with Instructions. CoRR abs/2211.09260 (2022) - [i13]Or Honovich, Thomas Scialom, Omer Levy, Timo Schick:
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. CoRR abs/2212.09689 (2022) - 2021
- [j1]Timo Schick, Sahana Udupa, Hinrich Schütze:
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP. Trans. Assoc. Comput. Linguistics 9: 1408-1424 (2021) - [c9]Timo Schick, Hinrich Schütze:
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. EACL 2021: 255-269 - [c8]Timo Schick, Hinrich Schütze:
Few-Shot Text Generation with Natural Language Instructions. EMNLP (1) 2021: 390-402 - [c7]Timo Schick, Hinrich Schütze:
Generating Datasets with Pretrained Language Models. EMNLP (1) 2021: 6943-6951 - [c6]Timo Schick, Hinrich Schütze:
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. NAACL-HLT 2021: 2339-2352 - [i12]Timo Schick, Sahana Udupa, Hinrich Schütze:
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP. CoRR abs/2103.00453 (2021) - [i11]Timo Schick, Hinrich Schütze:
Generating Datasets with Pretrained Language Models. CoRR abs/2104.07540 (2021) - [i10]Timo Schick, Hinrich Schütze:
True Few-Shot Learning with Prompts - A Real-World Perspective. CoRR abs/2111.13440 (2021) - 2020
- [c5]Timo Schick, Hinrich Schütze:
Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking. AAAI 2020: 8766-8774 - [c4]Timo Schick, Hinrich Schütze:
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. ACL 2020: 3996-4007 - [c3]Timo Schick, Helmut Schmid, Hinrich Schütze:
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. COLING 2020: 5569-5578 - [i9]Timo Schick, Hinrich Schütze:
Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference. CoRR abs/2001.07676 (2020) - [i8]Timo Schick, Hinrich Schütze:
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. CoRR abs/2009.07118 (2020) - [i7]Timo Schick, Helmut Schmid, Hinrich Schütze:
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. CoRR abs/2010.13641 (2020) - [i6]Timo Schick, Hinrich Schütze:
Few-Shot Text Generation with Pattern-Exploiting Training. CoRR abs/2012.11926 (2020)
2010 – 2019
- 2019
- [c2]Timo Schick, Hinrich Schütze:
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context. AAAI 2019: 6965-6973 - [c1]Timo Schick, Hinrich Schütze:
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts. NAACL-HLT (1) 2019: 489-494 - [i5]Timo Schick, Hinrich Schütze:
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts. CoRR abs/1904.01617 (2019) - [i4]Timo Schick, Hinrich Schütze:
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking. CoRR abs/1904.06707 (2019) - [i3]Timo Schick, Hinrich Schütze:
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. CoRR abs/1910.07181 (2019) - 2018
- [i2]Timo Schick, Hinrich Schütze:
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context. CoRR abs/1811.03866 (2018) - 2017
- [i1]Timo Schick:
Transition-Based Generation from Abstract Meaning Representations. CoRR abs/1707.07591 (2017)
Coauthor Index
aka: Jane A. Yu
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last updated on 2024-07-09 05:45 CEST by the dblp team
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