Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866907983465676800 |
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| author | Sun, Junwei Ma, Siqi Fan, Yiran Washington, Peter |
| author_facet | Sun, Junwei Ma, Siqi Fan, Yiran Washington, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts Sun, Junwei Ma, Siqi Fan, Yiran Washington, Peter Computation and Language Computers and Society Emerging Technologies Machine Learning 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. |
| title | Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts |
| topic | Computation and Language Computers and Society Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2407.13228 |