The Researchy Questions dataset provides about 100k question queries with complex information needs that require retrieving information about several aspects of a topic. Each query in ResearchyQuestions is associated with sub-questions that were produced by prompting GPT-4. While ResearchyQuestions contains labels indicating what documents were clicked after issuing the query, there are no associations in the dataset between sub-questions and relevant documents. In this work, we augment the Researchy Questions dataset with LLM-judged labels for each sub-question using a Llama3.3 70B model. We intend these sub-question labels to serve as a resource for training retrieval models that better support complex information needs.
@article{arxiv.2510.21733,
title = {Augmenting Researchy Questions with Sub-question Judgments},
author = {Jia-Huei Ju and Eugene Yang and Trevor Adriaanse and Andrew Yates},
journal= {arXiv preprint arXiv:2510.21733},
year = {2025}
}