Ori Ernst

ori.ernst@mila.quebec

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e

Jackie Cheung

Sujets de recherche

Apprentissage profond

Modèles génératifs

Traitement du langage naturel

Site web

Google Scholar

GitHub

Publications

Testing the Assumptions of Active Learning for Translation Tasks with Few Samples

Lorenzo Flores

Cesare Spinoso di-Piano

Ori Ernst

David Ifeoluwa Adelani

Jackie Chi Kit Cheung

Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which… (voir plus) is useful when only a limited number of samples can be annotated. These algorithms often work by optimizing for the informativeness and diversity of the training data to be annotated. Recent work found that AL strategies fail to outperform random sampling on various language generation tasks when using 100-500 samples. To understand AL's poor performance when only using few samples, we investigate whether the core assumptions underlying AL strategies hold. We find that neither the informativeness nor diversity of the training data, which AL strategies optimize for, are correlated with test set performance. Instead, factors like the ordering of the training samples and interactions with pre-training data have a larger impact on performance. This suggests that future AL methods must take these factors into account in order to work with very few samples.

2026-04-09

arXiv (prépublication)

doi.org

arxiv.org

PreSumm: Predicting Summarization Performance Without Summarizing

Steven Koniaev

Ori Ernst

Jackie Chi Kit Cheung

2024-12-31

Findings of the Association for Computational Linguistics: ACL (publié)

doi.org