MentalArena: Self-play Training of Language Models for Diagnosis and
  Treatment of Mental Health Disorders
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
67077c5ecbadd03eb3884d27
Cheng Li
claimed_verified
2024-10-10T07:58:37.011Z
6459f82e5b3111fbe8327aa0
/avatars/c377b442c9e2589ee0cc9be6bd028cc7.svg
Cheng Li
user
Cheng228
67077c5ecbadd03eb3884d28
May Fung
67077c5ecbadd03eb3884d29
Qingyun Wang
claimed_verified
2025-09-23T02:43:46.522Z
628ba530ac304a69264afb75
https://cdn-avatars.huggingface.co/v1/production/uploads/1653319185560-628ba530ac304a69264afb75.jpeg
Qingyun Wang
user
eaglew
67077c5ecbadd03eb3884d2a
Chi Han
claimed_verified
2025-03-18T08:12:37.081Z
645b11ff5987734177a7cdcf
/avatars/3b04d2ca166eb28f227fc8d800e55c1c.svg
Chi Han
user
Glaciohound
67077c5ecbadd03eb3884d2b
Manling Li
67077c5ecbadd03eb3884d2c
Jindong Wang
admin_assigned
2024-10-10T10:03:56.330Z
6204cc0d522e40b4a18d86e2
/avatars/18daf2de5671e711dc745388dd60569d.svg
Jindong Wang
user
jindongwang
67077c5ecbadd03eb3884d2d
Heng Ji
MentalArena: Self-play Training of Language Models for Diagnosis and
  Treatment of Mental Health Disorders
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
MentalArena, a self-play framework, generates personalized data to train language models for diagnosing and treating mental health disorders, outperforming existing models and addressing privacy concerns.
self-play framework language models Symptom Encoder Symptom Decoder patient-therapist interactions personalized care biomedicalQA mental health tasks GPT-3.5 Llama-3-8b GPT-4o