Timo Schick
23 papers · 2019–2024 · 10 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
π Renaissance Researcher (5) π Interdisciplinary Bridge π Conference Polyglot (10) π Academic Marathon (5) πΊοΈ Taxonomy Completionist (52)
π£
Hot Topic Early Bird
π
Cross-Pollinator
(15)
π
Renaissance Researcher
(5)
π
Keyword Champion
(3)
π§¬
Topic Evolution
π
Trend Setter
π
Conference Pioneer
π
Century Club
(23)
π₯
Unstoppable
(6)
β‘
Prolific Year
(8)
ποΈ
Keyword Collector
(82)
Conferences
EMNLP (7)
ACL (5)
AAAI (2)
ICLR (2)
NAACL (2)
COLING (1)
CONLL (1)
EACL (1)
JMLR (1)
NIPS (1)
Top co-authors
Keywords
few-shot learning
(8)
large language model
(5)
language model
(5)
instruction tuning
(4)
rare word
(3)
word embedding
(3)
pretrained language model
(3)
text classification
(3)
text editing
(2)
surface form
(2)
pattern-exploiting training
(2)
cloze question
(2)
active learning
(2)
dataset generation
(2)
text generation
(2)
question answering
(2)
zero-shot learning
(2)
natural language understanding
(2)
contextualized embedding
(2)
natural language inference
(1)
Papers
LongForm: Effective Instruction Tuning with Reverse Instructions
EMNLP 2024
EditEval: An Instruction-Based Benchmark for Text Improvements
CONLL 2024
EditEval: An Instruction-Based Benchmark for Text Improvements
EMNLP 2024
Self-Alignment with Instruction Backtranslation
ICLR 2024
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
ACL 2023
PEER: A Collaborative Language Model
ICLR 2023
Toolformer: Language Models Can Teach Themselves to Use Tools
NIPS 2023
Atlas: Few-shot Learning with Retrieval Augmented Language Models
JMLR 2023
MEAL: Stable and Active Learning for Few-Shot Prompting
EMNLP 2023
Active Learning Principles for In-Context Learning with Large Language Models
EMNLP 2023
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
ACL 2023
Task-aware Retrieval with Instructions
ACL 2023
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment
ACL 2022
Leveraging QA Datasets to Improve Generative Data Augmentation
EMNLP 2022
Itβs Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
NAACL 2021
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
EACL 2021
Few-Shot Text Generation with Natural Language Instructions
EMNLP 2021
Generating Datasets with Pretrained Language Models
EMNLP 2021
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
COLING 2020
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
ACL 2020
Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking
AAAI 2020
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
NAACL 2019
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
AAAI 2019