Summary of Comparing the Efficacy Of Gpt-4 and Chat-gpt in Mental Health Care: a Blind Assessment Of Large Language Models For Psychological Support, by Birger Moell
Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Support
by Birger Moell
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 This paper explores the application of large language models in improving mental healthcare outcomes. By leveraging these advanced NLP tools, researchers aim to enhance clinician-assisted diagnosis and treatment, as well as provide personalized support to individuals struggling with various psychological issues. The proposed approach utilizes deep learning architectures to analyze patient data and generate relevant insights, potentially leading to more effective interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special computer programs called large language models to help people with mental health problems. These programs can help doctors and therapists make better diagnoses and treatments, and also provide support to individuals experiencing stress, anxiety, or other emotional challenges. The goal is to develop a new way of using technology to improve mental healthcare. |
Keywords
» Artificial intelligence » Deep learning » Nlp
