  NLP systems have shown impressive performance at answering questions by
retrieving relevant context. However, with the increasingly large models, it is
impossible and often undesirable to constrain models' knowledge or reasoning to
only the retrieved context. This leads to a mismatch between the information
that the models access to derive the answer and the information that is
available to the user to assess the model predicted answer. In this work, we
study how users interact with QA systems in the absence of sufficient
information to assess their predictions. Further, we ask whether adding the
requisite background helps mitigate users' over-reliance on predictions. Our
study reveals that users rely on model predictions even in the absence of
sufficient information needed to assess the model's correctness. Providing the
relevant background, however, helps users better catch model errors, reducing
over-reliance on incorrect predictions. On the flip side, background
information also increases users' confidence in their accurate as well as
inaccurate judgments. Our work highlights that supporting users' verification
of QA predictions is an important, yet challenging, problem.
