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.
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
Traditional machine learning methods outperform state-of-the-art deep learning models, including transformer models and GPT-based approaches, in classifying anxiety and depression from conversational transcripts.
- Year
- 2024
- Venue
- arXiv 2024
- Authors
- 4
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2407.13228ARXIV-DEFAULT
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