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Prompt engineering for digital mental health: a short review
Y H P P Priyadarshana1, Ashala Senanayake1, Zilu Liang1
1Ubiquitous and Personal Computing Lab, Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Kyoto, Japan.
Prompt engineering guides large language models for digital mental health applications. This review details prompt types, methods, and tasks, offering a foundation for future research in this vital area.
Area of Science:
- Natural Language Processing (NLP)
- Digital Mental Health
- Artificial Intelligence (AI)
Background:
- Digital mental health is crucial for early detection and intervention, addressing limited access to clinical expertise.
- Prompt engineering is an emerging field that optimizes large language model (LLM) performance in various NLP tasks.
Purpose of the Study:
- To review the latest advances in prompt engineering for digital mental health applications.
- To be the first to discuss prompt engineering types, methods, and tasks specifically within digital mental health.
Main Methods:
- Literature review of recent prompt engineering techniques.
- Categorization of digital mental health tasks addressed by prompt engineering (classification, generation, question answering).
Main Results:
- Identification of key prompt engineering types and methods relevant to digital mental health.
- Overview of prompt engineering applications in classification, generation, and question answering tasks for mental health.
Conclusions:
- Prompt engineering offers significant potential for advancing digital mental health tools.
- Future research should address challenges, limitations, and ethical considerations in this domain.
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