241 citations · 456 across the 11 of their papers we have counts for
39 papers · 1 filter
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman +1
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can ex…
Breaking Character: Are Subwords Good Enough for MRLs After All?
Omri Keren, Tal Avinari, Reut Tsarfaty +1
Large pretrained language models (PLMs) typically tokenize the input string into contiguous subwords before any pretraining or inference. However, previous studies have claimed tha…
Are Mutually Intelligible Languages Easier to Translate?
Avital Friedland, Jonathan Zeltser, Omer Levy
Two languages are considered mutually intelligible if their native speakers can communicate with each other, while using their own mother tongue. How does the fact that humans perc…
A Few More Examples May Be Worth Billions of Parameters
Yuval Kirstain, Patrick Lewis, Sebastian Riedel +1
We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. Our exploration reveals that while sc…
ParaShoot: A Hebrew Question Answering Dataset
Omri Keren, Omer Levy
NLP research in Hebrew has largely focused on morphology and syntax, where rich annotated datasets in the spirit of Universal Dependencies are available. Semantic datasets, however…
Can Latent Alignments Improve Autoregressive Machine Translation?
Adi Haviv, Lior Vassertail, Omer Levy
Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the…