Timo Schick

发表

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

Gerard de Melo, Andrew M. Dai, Christopher D. Manning, 2022, ArXiv.

Improving Wikipedia verifiability with AI

Edouard Grave, Armand Joulin, M. Lomeli, 2022, Nature Machine Intelligence.

PEER: A Collaborative Language Model

Edouard Grave, Fabio Petroni, Patrick Lewis, 2022, ICLR.

Few-shot Learning with Retrieval Augmented Language Models

Jane A. Yu, Edouard Grave, Armand Joulin, 2022, J. Mach. Learn. Res..

EditEval: An Instruction-Based Benchmark for Text Improvements

Edouard Grave, M. Lomeli, Fabio Petroni, 2022, ArXiv.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Alexander M. Rush, Dragomir R. Radev, Ona de Gibert, 2022, ArXiv.

Task-aware Retrieval with Instructions

Hannaneh Hajishirzi, Xilun Chen, Patrick Lewis, 2022, 2211.09260.

MEAL: Stable and Active Learning for Few-Shot Prompting

Hinrich Schutze, Timo Schick, Abdullatif Köksal, 2022, ArXiv.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Alexander M. Rush, Dragomir R. Radev, Ona de Gibert, 2022, ArXiv.

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

Hinrich Schütze, Timo Schick, 2020, NAACL.

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

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2020, EACL.

Task-aware Retrieval with Instructions

Hannaneh Hajishirzi, Xilun Chen, Patrick Lewis, 2022, ACL.

Toolformer: Language Models Can Teach Themselves to Use Tools

Luke Zettlemoyer, M. Lomeli, Roberta Raileanu, 2023, NeurIPS.

Few-Shot Text Generation with Natural Language Instructions

Hinrich Schütze, Timo Schick, Hinrich Schütze, 2020, EMNLP.

Augmented Language Models: a Survey

Yann LeCun, Edouard Grave, Ramakanth Pasunuru, 2023, Trans. Mach. Learn. Res..

Few-Shot Text Generation with Pattern-Exploiting Training

Hinrich Schütze, Timo Schick, 2020, ArXiv.

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

Timo Schick, Sahana Udupa, Hinrich Schutze, 2021, Transactions of the Association for Computational Linguistics.

Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor

Omer Levy, Or Honovich, Timo Schick, 2022, ACL.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Alexander M. Rush, Dragomir R. Radev, Ona de Gibert, 2022, ArXiv.

Generating Datasets with Pretrained Language Models

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2021, EMNLP.

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

Helmut Schmid, Hinrich Schutze, Timo Schick, 2020, COLING.

MEAL: Stable and Active Learning for Few-Shot Prompting

Hinrich Schutze, Timo Schick, Abdullatif Köksal, 2022, ArXiv.

LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction

A. Korhonen, Hinrich Schütze, Timo Schick, 2023, ArXiv.

Semantic-Oriented Unlabeled Priming for Large-Scale Language Models

Hinrich Schütze, Yanchen Liu, Timo Schick, 2022, SUSTAINLP.

CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment

Lutfi Kerem Senel, Hinrich Schütze, Timo Schick, 2022, ACL.

Active Learning Principles for In-Context Learning with Large Language Models

Nikolaos Aletras, Jane Dwivedi-Yu, Timo Schick, 2023, EMNLP.

True Few-Shot Learning with Prompts—A Real-World Perspective

Timo Schick, Hinrich Schutze, Hinrich Schütze, 2021, Transactions of the Association for Computational Linguistics.

Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking

Hinrich Schütze, Timo Schick, Hinrich Schütze, 2019, AAAI.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Alexander M. Rush, Dragomir R. Radev, Ona de Gibert, 2022, ArXiv.

BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance

Hinrich Schütze, Timo Schick, 2019, ACL.

Leveraging QA Datasets to Improve Generative Data Augmentation

Jingbo Shang, Tu Vu, Timo Schick, 2022, EMNLP.

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

Ona de Gibert, David Ifeoluwa Adelani, M Saiful Bari, 2023, ArXiv.

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

Hinrich Schutze, Timo Schick, Hinrich Schütze, 2020, NAACL.

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

Ronan Le Bras, Stuart M. Shieber, Vinay Uday Prabhu, 2022, ArXiv.

Few-Shot Text Generation with Pattern-Exploiting Training pdf

Hinrich Schutze, Timo Schick, Hinrich Schütze, 2020, ArXiv.

Evaluation of Faithfulness Using the Longest Supported Subsequence

Mikel Artetxe, Anirudh Mittal, Jane Dwivedi-Yu, 2023, ArXiv.

CLoCE:Contrastive Learning Optimize Continous Prompt Embedding Space in Relation Extraction

Alec Radford, Yinhan Liu, Myle Ott, 2022 .

P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning

Teven Le Scao, Patrick von Platen, Quentin de Laroussilhe, 2021 .

MEAL: Stable and Active Learning for Few-Shot Prompting

Hinrich Schutze, Timo Schick, Abdullatif Köksal, 2022, EMNLP.

Few-shot Learning with Retrieval Augmented Language Models

Jane A. Yu, Edouard Grave, Armand Joulin, 2022, J. Mach. Learn. Res..