Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts

2024/07/18 by Siqi Ma, Sun, Junwei, Ma, Siqi +3
Psychology · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health Research Topics #Mental Health via Writing

paper · pdf · doi:10.48550/arxiv.2407.13228

openalex publication_date 2024/07/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We aim to evaluate the efficacy of traditional machine learning and large language models (LLMs) in classifying anxiety and depression from long conversational transcripts. We fine-tune both established transformer models (BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained a Support Vector Machine with feature engineering, and assessed GPT models through prompting. We observe that state-of-the-art models fail to enhance classification outcomes compared to traditional machine learning methods.

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