8 citations · 11 across the 5 of their papers we have counts for
4 papers · 1 filter
Few-Shot Text Generation with Pattern-Exploiting Training
Timo Schick, Hinrich Schütze
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined wit…
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
Timo Schick, Helmut Schmid, Hinrich Schütze
A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained langua…
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Timo Schick, Hinrich Schütze
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts…
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
Timo Schick, Hinrich Schütze
Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e.g., Radford et al., 2019). Whi…