arXiv:2603.06910v2 Announce Type: replace Abstract: Multilingual large language models (LLMs) are increasingly used in socially sensitive mental health contexts, including support chatbots, screening, and content moderation. This raises a reliability question: do semantically equivalent mental health inputs elicit comparable evaluations across languages, or systematic shifts consistent with language-associated social and cultural contexts? We examine this question in an English-Chinese setting with GPT-4o and Qwen3-32B using a two-level framework: construct-level evaluative orientation, measur
As multilingual LLMs expand into sensitive applications like mental health, understanding cultural and linguistic biases in their evaluations becomes critical for responsible deployment and trust.
This research highlights a significant challenge in deploying AI globally, where cultural nuances and language-specific contexts can lead to disparate and potentially harmful outcomes in critical applications.
The understanding that language itself can systematically alter how LLMs assess mental health, necessitating more rigorous, culturally-attuned development and evaluation frameworks for AI.
- · AI ethics researchers
- · Mental health tech startups focusing on culturally-nuanced AI
- · Organizations developing responsible AI guidelines
- · Companies deploying 'one-size-fits-all' global LLMs
- · Users relying on un-audited multilingual mental health AI
- · Developers neglecting cultural bias in model training
Increased scrutiny and demand for culturally competent AI models in sensitive sectors.
Development of new benchmarks and evaluation methods specifically designed to test for linguistic and cultural bias in AI.
Potential for regulatory frameworks to mandate cultural audits for AI systems deployed across different linguistic markets.
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Read at arXiv cs.CL