Summary of The Opportunities and Risks Of Large Language Models in Mental Health, by Hannah R. Lawrence et al.
The opportunities and risks of large language models in mental health
by Hannah R. Lawrence, Renee A. Schneider, Susan B. Rubin, Maja J. Mataric, Daniel J. McDuff, Megan Jones Bell
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel paper explores the potential of large language models (LLMs) in addressing the growing demand for mental health care. Building on existing applications of LLMs, this work summarizes the current state of research on using LLMs for mental health education, assessment, and intervention. The authors highlight key opportunities for positive impact, but also caution against risks associated with deploying these models without proper consideration. To mitigate these risks, they recommend fine-tuning LLMs for mental health, promoting equity, adhering to ethical standards, and involving people with lived experience in all stages of development. By prioritizing responsible development and deployment, the authors aim to maximize the positive impact of LLMs on global mental health. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help people with mental health issues. Right now, there are many more people who need mental health care than can get it. People are hoping that these big language models will be able to provide new and better ways to support mental health. The authors of this paper look at what’s been done so far in using these models for education, assessment, and helping people with mental health issues. They also talk about the risks involved and how we can make sure these models are used safely and effectively. |
Keywords
* Artificial intelligence * Fine tuning
