MentalArena：自我对弈训练语言模型用于诊断和治疗心理健康障碍。

MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders

October 9, 2024

作者: Cheng Li, May Fung, Qingyun Wang, Chi Han, Manling Li, Jindong Wang, Heng Ji

cs.AI

摘要

心理健康障碍是世界上最严重的疾病之一。大多数患有此类疾病的人缺乏获得充分护理的途径，这凸显了为心理健康障碍的诊断和治疗训练模型的重要性。然而，在心理健康领域，隐私问题限制了个性化治疗数据的获取，使得构建强大模型具有挑战性。在本文中，我们介绍了MentalArena，这是一个自我对弈框架，通过生成特定领域的个性化数据来训练语言模型，从而获得一个能够进行个性化诊断和治疗（作为治疗师）以及提供信息（作为患者）的更好模型。为了准确建模类似人类的心理健康患者，我们设计了症状编码器，从认知和行为角度模拟真实患者。为了解决患者-治疗师互动过程中的意图偏差，我们提出了症状解码器，用于比较诊断症状与编码症状，并根据识别的偏差动态管理患者和治疗师之间的对话。我们对MentalArena进行了评估，包括对生物医学问答和心理健康任务的6项基准测试，与6个先进模型进行了比较。我们的模型在经过GPT-3.5和Llama-3-8b的微调后，明显优于其对应的模型，包括GPT-4o。我们希望我们的工作能激发未来个性化护理研究。代码可在https://github.com/Scarelette/MentalArena/tree/main找到。

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