Tag: Findings – EMNLP

Hybrid Active Learning for Low-Resource LM Fine-tuning

We identified two key designs that can improve the effectiveness and efficiency of sample acquisition: random sampling reduces the unlabeled pool being considered for acquisition, and decouples the diversity and uncertainty objectives in hybrid acquisition. Based on an investigation of existing methods, we propose a novel active learning method: TYROGUE.

Read More »