  Existing question answering (QA) datasets are no longer challenging to most
powerful Large Language Models (LLMs). Traditional QA benchmarks like TriviaQA,
NaturalQuestions, ELI5 and HotpotQA mainly study ``known unknowns'' with clear
indications of both what information is missing, and how to find it to answer
the question. Hence, good performance on these benchmarks provides a false
sense of security. A yet unmet need of the NLP community is a bank of
non-factoid, multi-perspective questions involving a great deal of unclear
information needs, i.e. ``unknown uknowns''. We claim we can find such
questions in search engine logs, which is surprising because most
question-intent queries are indeed factoid. We present Researchy Questions, a
dataset of search engine queries tediously filtered to be non-factoid,
``decompositional'' and multi-perspective. We show that users spend a lot of
``effort'' on these questions in terms of signals like clicks and session
length, and that they are also challenging for GPT-4. We also show that ``slow
thinking'' answering techniques, like decomposition into sub-questions shows
benefit over answering directly. We release $\sim$ 100k Researchy Questions,
along with the Clueweb22 URLs that were clicked.
