  Machine learning models are prone to overfitting their training (source)
domains, which is commonly believed to be the reason why they falter in novel
target domains. Here we examine the contrasting view that multi-source domain
generalization (DG) is first and foremost a problem of mitigating source domain
underfitting: models not adequately learning the signal already present in
their multi-domain training data. Experiments on a reading comprehension DG
benchmark show that as a model learns its source domains better -- using
familiar methods such as knowledge distillation (KD) from a bigger model -- its
zero-shot out-of-domain utility improves at an even faster pace. Improved
source domain learning also demonstrates superior out-of-domain generalization
over three popular existing DG approaches that aim to limit overfitting. Our
implementation of KD-based domain generalization is available via PrimeQA at:
https://ibm.biz/domain-generalization-with-kd.
