@inproceedings{liu-etal-2026-tailored,
title = "Tailored Emotional {LLM}-Supporter: Enhancing Cultural Sensitivity",
author = "Liu, Chen Cecilia and
Arnaout, Hiba and
Kova{\v{c}}i{\'c}, Nils and
Atzil-Slonim, Dana and
Gurevych, Iryna",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.25/",
pages = "535--574",
ISBN = "979-8-89176-380-7",
abstract = "Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. In this work, we introduce , the first dataset designed for this task, spanning four cultures and including 1,729 distress messages, 1,523 cultural signals, and 1,041 support strategies with fine-grained emotional and cultural annotations. Leveraging , we (i) develop and test four adaptation strategies for guiding three state-of-the-art LLMs toward culturally sensitive responses; (ii) conduct comprehensive evaluations using LLM-as-a-Judge, in-culture human annotators, and clinical psychologists; (iii) show that adapted LLMs outperform anonymous online peer responses, and that simple cultural role-play is insufficient for cultural sensitivity; and (iv) explore the application of LLMs in clinical training, where experts highlight their potential in fostering cultural competence in novice therapists."
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<abstract>Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. In this work, we introduce , the first dataset designed for this task, spanning four cultures and including 1,729 distress messages, 1,523 cultural signals, and 1,041 support strategies with fine-grained emotional and cultural annotations. Leveraging , we (i) develop and test four adaptation strategies for guiding three state-of-the-art LLMs toward culturally sensitive responses; (ii) conduct comprehensive evaluations using LLM-as-a-Judge, in-culture human annotators, and clinical psychologists; (iii) show that adapted LLMs outperform anonymous online peer responses, and that simple cultural role-play is insufficient for cultural sensitivity; and (iv) explore the application of LLMs in clinical training, where experts highlight their potential in fostering cultural competence in novice therapists.</abstract>
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%0 Conference Proceedings
%T Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
%A Liu, Chen Cecilia
%A Arnaout, Hiba
%A Kovačić, Nils
%A Atzil-Slonim, Dana
%A Gurevych, Iryna
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F liu-etal-2026-tailored
%X Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. In this work, we introduce , the first dataset designed for this task, spanning four cultures and including 1,729 distress messages, 1,523 cultural signals, and 1,041 support strategies with fine-grained emotional and cultural annotations. Leveraging , we (i) develop and test four adaptation strategies for guiding three state-of-the-art LLMs toward culturally sensitive responses; (ii) conduct comprehensive evaluations using LLM-as-a-Judge, in-culture human annotators, and clinical psychologists; (iii) show that adapted LLMs outperform anonymous online peer responses, and that simple cultural role-play is insufficient for cultural sensitivity; and (iv) explore the application of LLMs in clinical training, where experts highlight their potential in fostering cultural competence in novice therapists.
%U https://aclanthology.org/2026.eacl-long.25/
%P 535-574
Markdown (Informal)
[Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity](https://aclanthology.org/2026.eacl-long.25/) (Liu et al., EACL 2026)
ACL
- Chen Cecilia Liu, Hiba Arnaout, Nils Kovačić, Dana Atzil-Slonim, and Iryna Gurevych. 2026. Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 535–574, Rabat, Morocco. Association for Computational Linguistics.