用大模型翻译并识别希腊语抑郁程度，发现效果受限需人工把关
Leveraging LLMs for Translating and Classifying Mental Health Data
- 将英文用户帖自动翻译为希腊语，用大模型检测抑郁严重度
- GPT3.5-turbo在英、希语中识别抑郁程度表现不一，准确率不高
- 提示多语言心理健康应用需谨慎，人工监督必不可少
大型语言模型（LLMs）在医疗领域应用日益广泛。在心理健康支持中，早期识别与心理疾病相关的语言特征，可为专业人员提供有力支持，并缩短患者等待时间。尽管大模型在心理健康支持中有潜力，但针对英语以外语言的应用研究仍有限。本研究填补了这一空白，聚焦于通过自动翻译英文用户生成内容至希腊语，来检测抑郁严重程度。结果表明，GPT3.5-turbo在英文中对抑郁严重度的识别表现不佳，在希腊语中也存在性能波动。研究强调，针对资源较少的语言需开展更多研究，且在心理健康平台中部署大模型时须审慎，人类监督对避免误诊至关重要。
原文摘要 · Abstract (English)
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.
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