  Models trained with empirical risk minimization (ERM) are revealed to easily
rely on spurious correlations, resulting in poor generalization. Group
distributionally robust optimization (group DRO) can alleviate this problem by
minimizing the worst-case loss over pre-defined groups. While promising, in
practice factors like expensive annotations and privacy preclude the
availability of group labels. More crucially, when taking a closer look at the
failure modes of out-of-distribution generalization, the typical procedure of
reweighting in group DRO loses efficiency. Hinged on the limitations, in this
work, we reformulate the group DRO framework by proposing Q-Diversity.
Characterized by an interactive training mode, Q-Diversity relaxes the group
identification from annotation into direct parameterization. Furthermore, a
novel mixing strategy across groups is presented to diversify the
under-represented groups. In a series of experiments on both synthetic and
real-world text classification tasks, results demonstrate that Q-Diversity can
consistently improve worst-case accuracy under different distributional shifts,
outperforming state-of-the-art alternatives.
