Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
<|reference_start|>Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts: 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.<|reference_end|>
@article{sun2024evaluating,
    title={Evaluating Large Language Models for Anxiety and Depression
  Classification using Counseling and Psychotherapy Transcripts},
    author={Junwei Sun, Siqi Ma, Yiran Fan, Peter Washington},
    journal={arXiv preprint arXiv:2407.13228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13228},
    primaryClass={cs.CL cs.CY cs.ET cs.LG}
}