@inproceedings{borah-etal-2026-curious,
title = "The Curious Case of Curiosity across Cultures: Evaluating Cross-Cultural Information-Seeking Questions in Humans and {LLM}s",
author = "Borah, Angana and
Jin, Zhijing and
Mihalcea, Rada",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.38/",
pages = "786--819",
ISBN = "979-8-89176-439-2",
abstract = "Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet information-seeking behavior, particularly across cultural contexts, remains underexplored in these systems. In this work, we study cross-cultural variation in information-seeking using question-asking as a mechanism. We introduce IQUEST (Information-seeking QUestion Evaluation across SocieTies), an evaluation framework that measures human-model alignment in information-seeking question behavior through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with question-asking patterns expressed in Western countries. We also explore fine-tuning strategies, which narrow the human-model alignment gap by 43{\%} overall. Finally, we demonstrate that stronger information-seeking question behavior improves downstream cultural adaptability, highlighting its importance for inclusive NLP research."
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%0 Conference Proceedings
%T The Curious Case of Curiosity across Cultures: Evaluating Cross-Cultural Information-Seeking Questions in Humans and LLMs
%A Borah, Angana
%A Jin, Zhijing
%A Mihalcea, Rada
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F borah-etal-2026-curious
%X Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet information-seeking behavior, particularly across cultural contexts, remains underexplored in these systems. In this work, we study cross-cultural variation in information-seeking using question-asking as a mechanism. We introduce IQUEST (Information-seeking QUestion Evaluation across SocieTies), an evaluation framework that measures human-model alignment in information-seeking question behavior through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with question-asking patterns expressed in Western countries. We also explore fine-tuning strategies, which narrow the human-model alignment gap by 43% overall. Finally, we demonstrate that stronger information-seeking question behavior improves downstream cultural adaptability, highlighting its importance for inclusive NLP research.
%U https://aclanthology.org/2026.aacl-long.38/
%P 786-819
Markdown (Informal)
[The Curious Case of Curiosity across Cultures: Evaluating Cross-Cultural Information-Seeking Questions in Humans and LLMs](https://aclanthology.org/2026.aacl-long.38/) (Borah et al., AACL-IJCNLP 2026)
ACL