Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
Chen Cecilia Liu, Hiba Arnaout, Nils Kova\v{c}i\'c, Dana Atzil-Slonim, Iryna Gurevych

TL;DR
This paper introduces CultureCare, a novel dataset for culturally sensitive emotional support by LLMs, and develops adaptation strategies that improve their performance in diverse cultural contexts, with applications in clinical training.
Contribution
The work presents the first dataset for culturally sensitive support, develops adaptation methods for LLMs, and evaluates their effectiveness compared to human responses and role-play.
Findings
Adapted LLMs outperform anonymous online peer responses.
Simple cultural role-play is insufficient for sensitivity.
LLMs show potential in clinical training for cultural competence.
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 CultureCare, the first dataset designed for this task, spanning four cultures and including 1729 distress messages, 1523 cultural signals, and 1041 support strategies with fine-grained emotional and cultural annotations. Leveraging CultureCare, 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…
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Taxonomy
TopicsMental Health via Writing · Cultural Competency in Health Care · Digital Mental Health Interventions
