Summary of Few-shot Learning For Mental Disorder Detection: a Continuous Multi-prompt Engineering Approach with Medical Knowledge Injection, by Haoxin Liu et al.
Few-Shot Learning for Mental Disorder Detection: A Continuous Multi-Prompt Engineering Approach with Medical Knowledge Injection
by Haoxin Liu, Wenli Zhang, Jiaheng Xie, Buomsoo Kim, Zhu Zhang, Yidong Chai, Sudha Ram
First submitted to arxiv on: 16 Jan 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 study, researchers utilize advanced AI technology to identify mental health conditions through user-generated text content. The conventional approach relies on fully supervised machine learning, which poses challenges such as the labor-intensive process of annotating extensive training data and designing specialized deep learning architectures for each task. The proposed method leverages large language models and continuous multi-prompt engineering, offering two key advantages: developing personalized prompts that capture individual characteristics and integrating structured medical knowledge into prompts to provide context for disease detection. The approach is evaluated using three prevalent mental disorders as case studies, demonstrating significant performance improvement over existing methods. Additionally, the method exhibits success in few-shot learning and can be generalized to other rare mental disorder detection tasks with minimal training examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses AI technology to detect mental health conditions through text messages. The researchers want to make it easier to identify these conditions by using big language models and special prompts that can understand each person’s unique characteristics. They also add medical knowledge to the prompts to help identify diseases better. The team tested their method on three common mental health conditions and found it worked much better than other methods. It even works with only a few examples! This could be helpful for people with mental health conditions and could make detection more accessible. |
Keywords
» Artificial intelligence » Deep learning » Few shot » Machine learning » Prompt » Supervised
