title: MentalArena: Self-play Training of Language Models for Diagnosis and
    Treatment of Mental Health Disorders
summary: 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
paper_authors._id: 67077c5ecbadd03eb3884d27
paper_authors.name: Cheng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T07:58:37.011Z
paper_authors._id: 6459f82e5b3111fbe8327aa0
paper_authors.avatarUrl: /avatars/c377b442c9e2589ee0cc9be6bd028cc7.svg
paper_authors.fullname: Cheng Li
paper_authors.type: user
paper_authors.user: Cheng228
paper_authors._id: 67077c5ecbadd03eb3884d28
paper_authors.name: May Fung
paper_authors._id: 67077c5ecbadd03eb3884d29
paper_authors.name: Qingyun Wang
paper_authors._id: 67077c5ecbadd03eb3884d2a
paper_authors.name: Chi Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T08:12:37.081Z
paper_authors._id: 645b11ff5987734177a7cdcf
paper_authors.avatarUrl: /avatars/3b04d2ca166eb28f227fc8d800e55c1c.svg
paper_authors.fullname: Chi Han
paper_authors.type: user
paper_authors.user: Glaciohound
paper_authors._id: 67077c5ecbadd03eb3884d2b
paper_authors.name: Manling Li
paper_authors._id: 67077c5ecbadd03eb3884d2c
paper_authors.name: Jindong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T10:03:56.330Z
paper_authors._id: 6204cc0d522e40b4a18d86e2
paper_authors.avatarUrl: /avatars/18daf2de5671e711dc745388dd60569d.svg
paper_authors.fullname: Jindong Wang
paper_authors.type: user
paper_authors.user: jindongwang
paper_authors._id: 67077c5ecbadd03eb3884d2d
paper_authors.name: Heng Ji
paper_title: MentalArena: Self-play Training of Language Models for Diagnosis and
    Treatment of Mental Health Disorders
paper_summary: 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
paper_ai_keywords: MentalArena self-play framework language models domain-specific personalized data personalized diagnosis personalized treatment Symptom Encoder cognition behavior Symptom Decoder intent bias dialogue management biomedicalQA mental health tasks GPT-3.5 Llama-3-8b GPT-4o