ALLWAS: Active Learning on Language models in WASserstein space
This addresses data scarcity and imbalance issues in domains like medicine, offering an incremental improvement over existing active learning approaches for language models.
The paper tackles the problem of limited labeled data and class imbalance in language models by proposing ALLWAS, a novel active learning method using submodular optimization and optimal transport, which achieves over 20% relative performance improvement on benchmark datasets.
Active learning has emerged as a standard paradigm in areas with scarcity of labeled training data, such as in the medical domain. Language models have emerged as the prevalent choice of several natural language tasks due to the performance boost offered by these models. However, in several domains, such as medicine, the scarcity of labeled training data is a common issue. Also, these models may not work well in cases where class imbalance is prevalent. Active learning may prove helpful in these cases to boost the performance with a limited label budget. To this end, we propose a novel method using sampling techniques based on submodular optimization and optimal transport for active learning in language models, dubbed ALLWAS. We construct a sampling strategy based on submodular optimization of the designed objective in the gradient domain. Furthermore, to enable learning from few samples, we propose a novel strategy for sampling from the Wasserstein barycenters. Our empirical evaluations on standard benchmark datasets for text classification show that our methods perform significantly better (>20% relative increase in some cases) than existing approaches for active learning on language models.