@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries
%A Ceraolo, Roberto
%A Kharlapenko, Dmitrii
%A Khan, Ahmad
%A Reymond, Amélie
%A Mihalcea, Rada
%A Schölkopf, Bernhard
%A Sachan, Mrinmaya
%A Jin, Zhijing
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F ceraolo-etal-2025-quriosity
%X 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.
%U https://aclanthology.org/2025.findings-ijcnlp.32/
%P 534-563
Markdown (Informal)
[Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries](https://aclanthology.org/2025.findings-ijcnlp.32/) (Ceraolo et al., Findings 2025)
ACL
- Roberto Ceraolo, Dmitrii Kharlapenko, Ahmad Khan, Amélie Reymond, Rada Mihalcea, Bernhard Schölkopf, Mrinmaya Sachan, and Zhijing Jin. 2025. Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries. In 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, pages 534–563, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.