Summary of Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales, By Wenyu Li et al.
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
by Wenyu Li, Yinuo Zhu, Xin Lin, Ming Li, Ziyue Jiang, Ziqian Zeng
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this research paper, the authors propose a new method for analyzing mental health using large language models (LLMs). The approach, called Mental Analysis by Incorporating Mental Scales (MAIMS), aims to address the limitations of traditional discriminative methods and generative approaches. MAIMS incorporates two procedures: first, patients complete mental scales, and second, psychologists interpret the collected information and make informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods and can generate more rigorous explanations based on the outputs of mental scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand people’s mental health using special scales and computers. The researchers created a method called MAIMS, which helps doctors make better decisions about patients’ mental well-being. Patients fill out the mental scales, and then doctors use this information to help their patients. This approach is better than other ways that don’t require much training data. |
Keywords
» Artificial intelligence » Zero shot
