ALVIN: Active Learning Via INterpolation
This addresses the issue of shortcut learning in active learning for NLP tasks, improving generalization, but it is incremental as it builds on existing active learning frameworks.
The paper tackles the problem of active learning methods overlooking distinct example groups within classes, which leads to models relying on spurious correlations, by proposing ALVIN, which uses intra-class interpolations to create anchors and select informative examples, resulting in outperforming state-of-the-art methods on six datasets for in-distribution and out-of-distribution generalization.
Active Learning aims to minimize annotation effort by selecting the most useful instances from a pool of unlabeled data. However, typical active learning methods overlook the presence of distinct example groups within a class, whose prevalence may vary, e.g., in occupation classification datasets certain demographics are disproportionately represented in specific classes. This oversight causes models to rely on shortcuts for predictions, i.e., spurious correlations between input attributes and labels occurring in well-represented groups. To address this issue, we propose Active Learning Via INterpolation (ALVIN), which conducts intra-class interpolations between examples from under-represented and well-represented groups to create anchors, i.e., artificial points situated between the example groups in the representation space. By selecting instances close to the anchors for annotation, ALVIN identifies informative examples exposing the model to regions of the representation space that counteract the influence of shortcuts. Crucially, since the model considers these examples to be of high certainty, they are likely to be ignored by typical active learning methods. Experimental results on six datasets encompassing sentiment analysis, natural language inference, and paraphrase detection demonstrate that ALVIN outperforms state-of-the-art active learning methods in both in-distribution and out-of-distribution generalization.