13 citations · 48 across the 26 of their papers we have counts for
5 papers · 1 filter
Demystifying Prompts in Language Models via Perplexity Estimation
Hila Gonen, Srini Iyer, Terra Blevins +2
Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do no…
Prompting Language Models for Linguistic Structure
Terra Blevins, Hila Gonen, Luke Zettlemoyer
Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable lin…
Few-shot Mining of Naturally Occurring Inputs and Outputs
Mandar Joshi, Terra Blevins, Mike Lewis +2
Creating labeled natural language training data is expensive and requires significant human effort. We mine input output examples from large corpora using a supervised mining funct…
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models
Terra Blevins, Hila Gonen, Luke Zettlemoyer
The emergent cross-lingual transfer seen in multilingual pretrained models has sparked significant interest in studying their behavior. However, because these analyses have focused…
Language Contamination Helps Explain the Cross-lingual Capabilities of English Pretrained Models
Terra Blevins, Luke Zettlemoyer
English pretrained language models, which make up the backbone of many modern NLP systems, require huge amounts of unlabeled training data. These models are generally presented as…