  Information-seeking questions in long-form question answering (LFQA) often
prove misleading due to ambiguity or false presupposition in the question.
While many existing approaches handle misleading questions, they are tailored
to limited questions, which are insufficient in a real-world setting with
unpredictable input characteristics. In this work, we propose PreWoMe, a
unified approach capable of handling any type of information-seeking question.
The key idea of PreWoMe involves extracting presuppositions in the question and
exploiting them as working memory to generate feedback and action about the
question. Our experiment shows that PreWoMe is effective not only in tackling
misleading questions but also in handling normal ones, thereby demonstrating
the effectiveness of leveraging presuppositions, feedback, and action for
real-world QA settings.
