Leveraging LLMs for Translating and Classifying Mental Health Data

TL;DR

This study explores the use of large language models for translating and classifying mental health data, highlighting challenges and the need for careful implementation, especially in less-resourced languages like Greek.

Contribution

It investigates the application of LLMs in mental health support for Greek, focusing on depression severity detection through translation, and emphasizes the importance of human oversight.

Findings

GPT-3.5-turbo performs poorly in depression severity detection in English.

Performance varies significantly when applied to Greek language data.

Further research is needed for effective use of LLMs in low-resource languages.

Abstract

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.