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

TL;DR

This study examines whether large language models can replicate culturally specific depression symptom expressions, finding they largely fail in English but show some alignment in Eastern languages, highlighting limitations in cultural sensitivity.

Contribution

It reveals the current limitations of general-purpose LLMs in capturing culturally grounded mental health symptom patterns, emphasizing the need for culture-aware AI models.

Findings

LLMs fail to replicate cultural depression patterns in English prompts.

Prompting in Eastern languages improves cultural alignment.

Models exhibit a culturally invariant symptom hierarchy overriding cues.

Abstract

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.