The model is trained end-to-end to answer common-sense-reasoning questions after generating explanations for its answers, using sample human explanations as par

The model is trained end-to-end to answer common-sense-reasoning questions after generating explanations for its answers, using sample human explanations as part of the training data. This results in improved performance on the question-answering task. This is fascinating, although in hindsight, not entirely surprising: inducing a machine to learn to model human explanations helps the machine perform better in testing. A natural question follows: Can we find ways to induce much larger models to learn to generate human explanations about a growing number of subjects of increasing complexity?