Summary of Mentalarena: Self-play Training Of Language Models For Diagnosis and Treatment Of Mental Health Disorders, by Cheng Li et al.
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders
by Cheng Li, May Fung, Qingyun Wang, Chi Han, Manling Li, Jindong Wang, Heng Ji
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 The proposed MentalArena framework trains language models for diagnosing and treating mental health disorders by generating personalized data. The model, fine-tuned on GPT-3.5 and Llama-3-8b, outperforms advanced models in biomedicalQA and mental health tasks. A Symptom Encoder simulates real patients from cognition and behavior perspectives, while a Symptom Decoder addresses intent bias during patient-therapist interactions. The framework’s evaluation against six benchmarks demonstrates its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mental health disorders are a major global problem, but many people struggle to get the help they need due to lack of access to quality care. One way to improve this is by training models that can diagnose and treat mental health issues. However, there’s a big challenge: personal data related to mental health treatment is often private, making it hard to create powerful models. To solve this problem, researchers created MentalArena, a system that generates personalized data for training language models. This allows the models to make personalized diagnoses and provide therapy. The team also developed tools like Symptom Encoder and Symptom Decoder to make sure the model accurately represents real people and doesn’t have biases. They tested their approach against six other models and found it was more effective. |
Keywords
» Artificial intelligence » Decoder » Encoder » Gpt » Llama
