@inproceedings{li-etal-2026-culturetalk,
title = "{C}ulture{T}alk: Towards Multi-turn Cultural Competence in Large Language Models",
author = "Li, Minzhi and
Han, Jiayu and
Poliannikov, Oleg and
Agarwal, Vipul and
Hong, Dezhi and
Wang, Mingxian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.99/",
pages = "2048--2065",
ISBN = "979-8-89176-462-0",
abstract = "As conversational AI systems increasingly engage users through multi-turn dialogue across diverse cultural contexts, cross-cultural competence, the ability to recognize, respect, and adapt to cultural differences, is essential for effective human-AI interaction. Existing evaluations largely rely on single-turn settings, overlooking the complexities of real-world, multi-turn conversations where cultural cues often emerge gradually. To address this gap, we introduce CultureTalk, a benchmark for evaluating multi-turn cultural reasoning in conversational AI. The dataset contains 1,741 culturally grounded dialogues derived from human-verified cultural knowledge resources and evaluates models' ability to accumulate cultural context across turns and remain robust to topic shifts. Our evaluation reveals substantial performance gaps between single- and multi-turn settings, highlighting current models' limitations in sustained culturally grounded context understanding. We further show that fine-tuning on augmented single-turn conversational data and inference scaling significantly improve multi-turn cultural reasoning, yielding a 64.7{\%} relative gain in cultural knowledge entailment with a fine-tuned Qwen3-8B model."
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<abstract>As conversational AI systems increasingly engage users through multi-turn dialogue across diverse cultural contexts, cross-cultural competence, the ability to recognize, respect, and adapt to cultural differences, is essential for effective human-AI interaction. Existing evaluations largely rely on single-turn settings, overlooking the complexities of real-world, multi-turn conversations where cultural cues often emerge gradually. To address this gap, we introduce CultureTalk, a benchmark for evaluating multi-turn cultural reasoning in conversational AI. The dataset contains 1,741 culturally grounded dialogues derived from human-verified cultural knowledge resources and evaluates models’ ability to accumulate cultural context across turns and remain robust to topic shifts. Our evaluation reveals substantial performance gaps between single- and multi-turn settings, highlighting current models’ limitations in sustained culturally grounded context understanding. We further show that fine-tuning on augmented single-turn conversational data and inference scaling significantly improve multi-turn cultural reasoning, yielding a 64.7% relative gain in cultural knowledge entailment with a fine-tuned Qwen3-8B model.</abstract>
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%0 Conference Proceedings
%T CultureTalk: Towards Multi-turn Cultural Competence in Large Language Models
%A Li, Minzhi
%A Han, Jiayu
%A Poliannikov, Oleg
%A Agarwal, Vipul
%A Hong, Dezhi
%A Wang, Mingxian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-culturetalk
%X As conversational AI systems increasingly engage users through multi-turn dialogue across diverse cultural contexts, cross-cultural competence, the ability to recognize, respect, and adapt to cultural differences, is essential for effective human-AI interaction. Existing evaluations largely rely on single-turn settings, overlooking the complexities of real-world, multi-turn conversations where cultural cues often emerge gradually. To address this gap, we introduce CultureTalk, a benchmark for evaluating multi-turn cultural reasoning in conversational AI. The dataset contains 1,741 culturally grounded dialogues derived from human-verified cultural knowledge resources and evaluates models’ ability to accumulate cultural context across turns and remain robust to topic shifts. Our evaluation reveals substantial performance gaps between single- and multi-turn settings, highlighting current models’ limitations in sustained culturally grounded context understanding. We further show that fine-tuning on augmented single-turn conversational data and inference scaling significantly improve multi-turn cultural reasoning, yielding a 64.7% relative gain in cultural knowledge entailment with a fine-tuned Qwen3-8B model.
%U https://aclanthology.org/2026.findings-emnlp.99/
%P 2048-2065
Markdown (Informal)
[CultureTalk: Towards Multi-turn Cultural Competence in Large Language Models](https://aclanthology.org/2026.findings-emnlp.99/) (Li et al., Findings 2026)
ACL