8 citations · 11 across the 4 of their papers we have counts for
14 papers · 1 filter
EditEval: An Instruction-Based Benchmark for Text Improvements
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang +6
Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text. Writing, however, is naturally an iterativ…
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment
Lütfi Kerem Senel, Timo Schick, Hinrich Schütze
Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a c…
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Yanchen Liu, Timo Schick, Hinrich Schütze
Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-contex…
Generating Datasets with Pretrained Language Models
Timo Schick, Hinrich Schütze
To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set…
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Timo Schick, Sahana Udupa, Hinrich Schütze
When trained on large, unfiltered crawls from the internet, language models pick up and reproduce all kinds of undesirable biases that can be found in the data: they often generate…
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…