Abstract
This study experimentally tested whether language and cultural prompts would influence large language models' (LLMs) assessments and recommendations when presented with the same mental health description. We conducted two experiments employing a 2 (language prompting: Chinese vs. English) x 2 (cultural prompting: China vs. the United States) factorial design using ChatGPT-4o and DeepSeek-V3. Across all experimental conditions, we used the same key message that implied a sign of mild depression from a friend. The language prompting was manipulated by presenting the whole scenario in English or in Chinese. The cultural prompting was manipulated by specifying that LLMs' responses should emulate human responses and should be consistent with the linguistic and cultural background of those who live in China or the United States. In both the ChatGPT-4o and DeepSeek-V3 conditions, using English rather than Chinese significantly elevated LLMs' evaluations of the friend's level of depression and the likelihood that LLMs recommend the friend to see a therapist. Moreover, while contextualizing the interaction in the United States rather than China lowered ChatGPT-4o's assessment of depression, it improved DeepSeek-V3's assessments of depression and its recommendation likelihood. LLMs' assessments of a mental health issue and their recommendations are shaped by the linguacultural features presented in the prompt. The present study innovatively extended the tests of the language-priming-culture hypothesis from human-to-human interactions to human-machine interactions, highlighting the language and cultural biases embedded in LLMs. The findings bring significant implications for bilingual speakers who may interact with LLMs using different languages.