Summary of A Comprehensive Evaluation Of Large Language Models on Mental Illnesses, by Abdelrahman Hanafi et al.
A Comprehensive Evaluation of Large Language Models on Mental Illnesses
by Abdelrahman Hanafi, Mohammed Saad, Noureldin Zahran, Radwa J. Hanafy, Mohammed E. Fouda
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have shown promise in various domains, including healthcare. This study conducts a comprehensive evaluation of various LLMs on mental health tasks using social media data, exploring zero-shot and few-shot capabilities on tasks such as binary disorder detection, disorder severity evaluation, and psychiatric knowledge assessment. Key findings reveal that some models like GPT-4 and Llama 3 exhibit superior performance in binary disorder detection, with accuracies reaching up to 85%. Prompt engineering plays a crucial role in enhancing model performance, and few-shot learning improves accuracy in complex assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being tested on mental health tasks using social media data. Researchers looked at many different models’ abilities to perform tasks like detecting mental health disorders, evaluating how severe they are, and testing their knowledge of psychiatry. They found that some models, like GPT-4 and Llama 3, are very good at detecting mental health disorders with high accuracy. The way the questions are asked also makes a big difference in how well the models do. |
Keywords
» Artificial intelligence » Few shot » Gpt » Llama » Prompt » Zero shot
