MentalArena: Self-play Training of Language Models for Diagnosis and
  Treatment of Mental Health Disorders
Authors: name: Cheng Li, name: May Fung, name: Qingyun Wang, name: Chi Han, name: Manling Li, name: Jindong Wang, name: Heng Ji

Mental health disorders are one of the most serious diseases in the world.
Most people with such a disease lack access to adequate care, which highlights
the importance of training models for the diagnosis and treatment of mental
health disorders. However, in the mental health domain, privacy concerns limit
the accessibility of personalized treatment data, making it challenging to
build powerful models. In this paper, we introduce MentalArena, a self-play
framework to train language models by generating domain-specific personalized
data, where we obtain a better model capable of making a personalized diagnosis
and treatment (as a therapist) and providing information (as a patient). To
accurately model human-like mental health patients, we devise Symptom Encoder,
which simulates a real patient from both cognition and behavior perspectives.
To address intent bias during patient-therapist interactions, we propose
Symptom Decoder to compare diagnosed symptoms with encoded symptoms, and
dynamically manage the dialogue between patient and therapist according to the
identified deviations. We evaluated MentalArena against 6 benchmarks, including
biomedicalQA and mental health tasks, compared to 6 advanced models. Our
models, fine-tuned on both GPT-3.5 and Llama-3-8b, significantly outperform
their counterparts, including GPT-4o. We hope that our work can inspire future
research on personalized care. Code is available in
https://github.com/Scarelette/MentalArena/tree/main