Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
<|reference_start|>Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents: 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.<|reference_end|>
@article{rosset2024researchy,
    title={Researchy Questions: A Dataset of Multi-Perspective, Decompositional
  Questions for LLM Web Agents},
    author={Corby Rosset, Ho-Lam Chung, Guanghui Qin, Ethan C. Chau, Zhuo Feng,
  Ahmed Awadallah, Jennifer Neville, Nikhil Rao},
    journal={arXiv preprint arXiv:2402.17896},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17896},
    primaryClass={cs.CL cs.AI}
}