Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients. Despite the benefits of LLMs for mental health support, there is limited research on their application in mental health systems for languages other than English. Our study addresses this gap by focusing on the detection of depression severity in Greek through user-generated posts which are automatically translated from English. Our results show that GPT3.5-turbo is not very successful in identifying the severity of depression in English, and it has a varying performance in Greek as well. Our study underscores the necessity for further research, especially in languages with less resources. Also, careful implementation is necessary to ensure that LLMs are used effectively in mental health platforms, and human supervision remains crucial to avoid misdiagnosis.

本研究解决了大语言模型在非英语心理健康系统应用中的研究缺口，重点检测希腊语中用户生成帖子所反映的抑郁严重性。研究结果表明，GPT3.5-turbo在识别英语抑郁严重性方面效果不佳，希腊语表现也存在差异，强调了在资源较少语言中进行进一步研究的必要性及人类监督的重要性。

利用大语言模型翻译与分类心理健康数据