@inproceedings{srikanth-etal-2025-questions,
    title = "No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions",
    author = "Srikanth, Neha  and
      Rudinger, Rachel  and
      Boyd-Graber, Jordan Lee",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.160/",
    pages = "3182--3199",
    ISBN = "979-8-89176-251-0",
    abstract = "Questions help unlock information to satisfy users' information needs. However, when the question is poorly posed, answerers (whether human or computer) may struggle to answer the question in a way that satisfies the asker, despite possibly knowing everything necessary to address the asker{'}s latent information need. Using Reddit question-answer interactions from r/NoStupidQuestions, we develop a computational framework grounded in linguistic theory to study poorly-posedness of questions by generating spaces of potential interpretations of questions and computing distributions over these spaces based on interpretations chosen by both human answerers in the Reddit question thread, as well as by a suite of large language models. Both humans and models struggle to converge on dominant interpretations when faced with poorly-posed questions, but employ different strategies: humans focus on specific interpretations through question negotiation, while models attempt comprehensive coverage by addressing many interpretations simultaneously."
}