@inproceedings{ceraolo-etal-2025-quriosity,
    title = "Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries",
    author = {Ceraolo, Roberto  and
      Kharlapenko, Dmitrii  and
      Khan, Ahmad  and
      Reymond, Am{\'e}lie  and
      Mihalcea, Rada  and
      Sch{\"o}lkopf, Bernhard  and
      Sachan, Mrinmaya  and
      Jin, Zhijing},
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.32/",
    pages = "534--563",
    ISBN = "979-8-89176-303-6",
    abstract = "Recent progress in Large Language Model (LLM) technology has changed our role in interacting with these models. Instead of primarily testing these models with questions we already know answers to, we are now using them for queries where the answers are unknown to us, driven by human curiosity. This shift highlights the growing need to understand curiosity-driven human questions {--} those that are more complex, open-ended, and reflective of real-world needs. To this end, we present Quriosity, a collection of 13K naturally occurring questions from three diverse sources: human-to-search-engine queries, human-to-human interactions, and human-to-LLM conversations. Our comprehensive collection enables a rich understanding of human curiosity across various domains and contexts. Our analysis reveals a significant presence of causal questions (up to 42{\%}) in the dataset, for which we develop an iterative prompt improvement framework to identify all causal queries and examine their unique linguistic properties, cognitive complexity and source distribution. We also lay the groundwork for exploring efficient identifiers of causal questions, providing six efficient classification models."
}