  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.
