Leveraging LLMs for Translating and Classifying Mental Health Data

2024/10/16 by Konstantinos Skianis, Skianis, Konstantinos, A. Seza Doğruöz +3 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2410.12985

openalex publication_date 2024/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• In-context learning capabilities of Large Language Models to detect suicide risk among adolescents from speech transcripts
• A Survey on Multilingual Mental Disorders Detection from Social Media Data

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