Portrait de Lorenzo Flores

Lorenzo Flores

lorenzo.flores@mila.quebec
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Jackie Cheung
Sujets de recherche
Apprentissage actif
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