mlsanityData to 14 Oct 2026

Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs

arXiv 2508.03247 ↗First posted 5 Aug 2025cs.CL cs.CYcited by 2

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Abstract (arXiv)

Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals report somatic ones. We test whether Large Language Models (LLMs), which are increasingly used in mental health, reproduce these cultural patterns by prompting them with Western or Eastern personas. Results show that LLMs largely fail to replicate the patterns when prompted in English, though prompting in major Eastern languages (i.e., Chinese, Japanese, and Hindi) improves alignment in several configurations. Our analysis pinpoints two key reasons for this failure: the models' low sensitivity to cultural personas and a strong, culturally invariant symptom hierarchy that overrides cultural cues. These findings reveal that while prompt language is important, current general-purpose LLMs lack the robust, culture-aware capabilities essential for safe and effective mental health applications.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 60 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

21 of 60 references.

References · 21

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 206
  3. 2024Investigating Cultural Alignment of Large Language Modelscited by 143
  4. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  5. 2024Safety Layers in Aligned Large Language Models: The Key to LLM Securitycited by 134
  6. 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
  7. 2023NLPositionality: Characterizing Design Biases of Datasets and Modelscited by 94
  8. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
  9. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  10. 2024The opportunities and risks of large language models in mental healthcited by 54
  11. 2023Evaluation of ChatGPT for NLP-based Mental Health Applicationscited by 53
  12. 2024How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensionscited by 52
9 more
  1. 2023Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Modelscited by 31
  2. 2024PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluationcited by 26
  3. 2023Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionalscited by 24
  4. 2023Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Modelscited by 24
  5. 2022Symptom Identification for Interpretable Detection of Multiple Mental Disorderscited by 20
  6. 2022Exploring The Design of Prompts For Applying GPT-3 based Chatbots: A Mental Wellbeing Case Study on Mechanical Turkcited by 13
  7. 2024Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twittercited by 12
  8. 2024Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labelscited by 10
  9. 2024Cross-Cultural Differences in Mental Health Expressions on Social Mediacited by 8

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Citing papers per year: 2026: 2.

2 papers.