assumption verification

Naturally occurring information-seeking questions often contain questionable assumptions—assumptions that are false or unverifiable. Questions containing questionable assumptions are challenging because they require a distinct answer strategy that deviates from typical answers for information-seeking questions. For instance, the question When did Marie Curie discover Uranium? cannot be answered as a typical when question without addressing the false assumption Marie Curie discovered Uranium. In this work, we propose (QA)² (Question Answering with Questionable Assumptions), an open-domain evaluation dataset consisting of naturally occurring search engine queries that may or may not contain questionable assumptions. To be successful on (QA)², systems must be able to detect questionable assumptions and also be able to produce adequate responses for both typical information-seeking questions and ones with questionable assumptions. Through human rater acceptability on end-to-end QA with (QA)², we find that current models do struggle with handling questionable assumptions, leaving substantial headroom for progress.