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

Junwei Sun, Siqi Ma, Yiran Fan, Peter Washington
7/18/2024
cs.CLcs.CYcs.ET

No official code found. Best implementation: ivysun14/Mental-Health-Prediction (4 stars, last commit 2025-11-16).

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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This repository contains all scripts used in the manuscript: Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts.

40Python, Jupyter NotebookJun 22, 202411 months ago

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Cite this paper

@article{sun2024evaluating,
  title  = {Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts},
  author = {Junwei Sun and Siqi Ma and Yiran Fan and Peter Washington},
  year   = {2024},
  eprint = {2407.13228},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.13228v1}
}

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