Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
- URL: http://arxiv.org/abs/2508.07902v1
- Date: Mon, 11 Aug 2025 12:17:58 GMT
- Title: Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
- Abstract summary: Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress.<n>But their ability to deliver culturally sensitive support remains underexplored due to lack of resources.<n>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.
- Score: 44.273123155989715
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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 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 judges, 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 future therapists.
Related papers
- CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs [59.09971492475217]
We introduce CultureForest, a benchmark for textitCultural Norm Grounded Reasoning<n>CultureForest comprises 5,378 examples across 8 domains and 53 countries/regions.<n>Extensive experiments reveal that even top-tier models degrade substantially in open-ended settings.
arXiv Detail & Related papers (2026-06-01T08:25:12Z) - CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs [13.267566050457974]
Cross-cultural knowledge insertion focuses on adapting models to specific cultural contexts while preserving original behavior in other cultures.<n> benchmark includes 9,800 image-grounded cases covering 49 culturally relevant visual scenarios across English, Chinese, and Arabic language-culture groups.<n>Memory-Conditioned Knowledge Insertion (MCKI) retrieves relevant cultural knowledge from an external memory using frozen MLLM representations.
arXiv Detail & Related papers (2026-05-07T12:26:11Z) - Cultural Prompting Improves the Empathy and Cultural Responsiveness of GPT-Generated Therapy Responses [14.378854402393829]
Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support.<n>This study evaluated the effectiveness of cultural prompting in improving cultural responsiveness and perceived empathy.
arXiv Detail & Related papers (2025-10-19T02:09:29Z) - Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World [68.19795061447044]
This paper investigates cross-cultural transfer of commonsense reasoning in the Arab world.<n>Using a culturally grounded commonsense reasoning dataset covering 13 Arab countries, we evaluate lightweight alignment methods.<n>Our results show that merely 12 culture-specific examples from one country can improve performance in others by 10% on average.
arXiv Detail & Related papers (2025-09-23T17:24:14Z) - Entangled in Representations: Mechanistic Investigation of Cultural Biases in Large Language Models [42.367959511140036]
We propose Culturescope, the first interpretability-based method that probes the internal representations of large language models.<n>We introduce a cultural flattening score as a measure of the intrinsic cultural biases.<n>Our experimental results reveal that LLMs encode Western-dominance bias and cultural flattening in their cultural knowledge space.
arXiv Detail & Related papers (2025-08-12T12:05:32Z) - CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization [50.90288681622152]
Large Language Models (LLMs) more deeply integrate into human life across various regions.<n>Existing approaches develop culturally aligned LLMs through fine-tuning with culture-specific corpora.<n>We introduce CAReDiO, a novel cultural data construction framework.
arXiv Detail & Related papers (2025-04-09T13:40:13Z) - Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions [9.357186653223332]
We evaluate the capacity of Large Language Models to recognize and accurately respond to the Little Traditions within Indian society.<n>Through a series of case studies, we assess whether LLMs can balance the interplay between dominant Great Traditions and localized Little Traditions.<n>Our findings reveal that while LLMs demonstrate an ability to articulate cultural nuances, they often struggle to apply this understanding in practical, context-specific scenarios.
arXiv Detail & Related papers (2025-01-28T06:58:25Z) - CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding.<n>CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types.<n>We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
arXiv Detail & Related papers (2025-01-02T14:42:37Z) - Survey of Cultural Awareness in Language Models: Text and Beyond [39.77033652289063]
Large-scale deployment of large language models (LLMs) in various applications requires LLMs to be culturally sensitive to the user to ensure inclusivity.
Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive.
arXiv Detail & Related papers (2024-10-30T16:37:50Z) - Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models [4.771099208181585]
LLMs are increasingly deployed in global applications, ensuring users from diverse backgrounds feel respected and understood.<n>Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values.<n>We present two key contributions: A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and a culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators.
arXiv Detail & Related papers (2024-10-15T18:13:10Z) - CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection.
It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs.
We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
arXiv Detail & Related papers (2024-05-24T01:49:02Z) - Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
Large language models (LLMs) have demonstrated substantial commonsense understanding.
This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
arXiv Detail & Related papers (2024-05-07T20:28:34Z) - CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge [69.82940934994333]
We introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build challenging evaluation dataset.
Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions.
CULTURALBENCH-V0.1 is a compact yet high-quality evaluation dataset with users' red-teaming attempts.
arXiv Detail & Related papers (2024-04-10T00:25:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.