3 papers
cs.CL2026
Testing the Assumptions of Active Learning for Translation Tasks with Few Samples
Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Ori Ernst +2
Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful when only a limited number of…
cs.CL2026
Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning
Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Jackie Chi Kit Cheung
Uncertainty quantification is a set of techniques that measure confidence in language models. They can be used, for example, to detect hallucinations or alert users to review uncer…
cs.CL2025
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing
Behzad Shayegh, Hobie H. -B. Lee, Xiaodan Zhu +2
We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe t…