用自对弈生成心理诊疗数据，训练出能当医生和病人对话的AI模型。
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders
- 通过自生成患者与治疗师交互数据，解决隐私限制下的个性化训练难题。
- 在6个基准上优于GPT-3.5、Llama-3-8b及GPT-4o，尤其在心理诊疗任务中表现突出。
- 适合研究个性化医疗AI、心理辅助系统或自对弈训练方法的学者与开发者。
精神健康障碍是全球最严重的疾病之一，但多数患者难以获得足够治疗，凸显了构建诊疗模型的重要性。然而，隐私问题限制了个性化治疗数据的获取，阻碍了强模型的建立。本文提出MentalArena，一个基于自对弈的框架，通过生成领域特定的个性化数据来训练语言模型，使其能扮演治疗师进行个性化诊断与治疗，同时扮演患者提供信息。为真实模拟患者认知与行为，我们设计了症状编码器（Symptom Encoder）；为缓解交互中的意图偏差，提出症状解码器（Symptom Decoder），根据症状差异动态调整对话。我们在6个基准上评估，涵盖biomedicalQA与心理健康任务，对比6个先进模型。在GPT-3.5与Llama-3-8b基础上微调的模型显著优于其对手，包括GPT-4o。代码已公开于https://github.com/Scarelette/MentalArena/tree/main。
原文摘要 · Abstract (English)
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
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