Recent work in Natural Language Processing has focused on developing
approaches that extract faithful explanations, either via identifying the most
important tokens in the input (i.e. post-hoc explanations) or by designing
inherently faithful models that first select the most important tokens and then
use them to predict the correct label (i.e. select-then-predict models).
Currently, these approaches are largely evaluated on in-domain settings. Yet,
little is known about how post-hoc explanations and inherently faithful models
perform in out-of-domain settings. In this paper, we conduct an extensive
empirical study that examines: (1) the out-of-domain faithfulness of post-hoc
explanations, generated by five feature attribution methods; and (2) the
out-of-domain performance of two inherently faithful models over six datasets.
Contrary to our expectations, results show that in many cases out-of-domain
post-hoc explanation faithfulness measured by sufficiency and comprehensiveness
is higher compared to in-domain. We find this misleading and suggest using a
random baseline as a yardstick for evaluating post-hoc explanation
faithfulness. Our findings also show that select-then predict models
demonstrate comparable predictive performance in out-of-domain settings to
full-text trained models.

该论文探索了自然语言处理中有关如何在不同领域中提取精准的解释的方法，其中侧重于后续解释和固有的忠诚度模型，并且发现后续解释的忠诚度在许多情况下比在领域内更高，而在跨域设置中，选择后预测模型的预测性能与完整训练模型相当。