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. 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 unknowns''. We claim we can find such questions in search engine logs, which is surprising because most question-intent queries are indeed factoid. Furthermore, recent products like Google's DeepResearch (announced a year after this resource was released publicly) specifically address such queries, retrieving hundreds of documents to synthesize report-style responses. We present Researchy Questions, the world's first, only and largest public dataset of ''Deep Research'' questions filtered from real search engine logs to be non-factoid, ''decompositional'' and multi-perspective. We show that users spend substantial ''effort'' on these questions in terms of signals like clicks and session length. We also show that ''slow thinking'' answering techniques, like decomposition into sub-questions shows benefit over answering directly. We release (at https://huggingface.co/datasets/corbyrosset/researchy_questions) about 100k Researchy Questions with a permissive CDLA-2.0 license, along with click histograms on over 350k Clueweb22 URLs that were clicked for each question.

This paper studies a new scenario in conversational search, conversational question suggestion, which leads search engine users to more engaging experiences by suggesting interesting, informative, and useful follow-up questions. We first establish a novel evaluation metric, usefulness, which goes beyond relevance and measures whether the suggestions provide valuable information for the next step of a user’s journey, and construct a public benchmark for useful question suggestion. Then we develop two suggestion systems, a BERT based ranker and a GPT-2 based generator, both trained with novel weak supervision signals that convey past users’ search behaviors in search sessions. The weak supervision signals help ground the suggestions to users’ information-seeking trajectories: we identify more coherent and informative sessions using encodings, and then weakly supervise our models to imitate how users transition to the next state of search. Our offline experiments demonstrate the crucial role our “next-turn” inductive training plays in improving usefulness over a strong online system. Our online A/B test in Bing shows that our more useful question suggestions receive 8% more user clicks than the previous system.