MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders (2410.06845v1)

Abstract: 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 LLMs 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

Essay on "MentalArena: Self-play Training of LLMs for Diagnosis and Treatment of Mental Health Disorders"

The paper presents a sophisticated framework named MentalArena, designed to enhance the training of LLMs specifically for diagnosing and treating mental health disorders. The primary innovation lies in leveraging a self-play approach, wherein models simulate complex interactions between patients and therapists to generate training data that is both domain-specific and personalized. This technique addresses two significant challenges: the scarcity of high-quality, personalized mental health data and intent bias during patient-therapist dialogues.

Key Contributions

1. Symptom Encoder and Decoder: The paper introduces two novel components—Symptom Encoder and Symptom Decoder. The Symptom Encoder uses cognitive and behavioral principles to synthesize realistic patient data, simulating human-like mental health cases. The Symptom Decoder mitigates intent biases by aligning diagnosed symptoms with encoded symptoms, thus guiding interactions more effectively.
2. Robust Evaluation: MentalArena's effectiveness has been evaluated extensively on six benchmarks, encompassing biomedical question answering (QA) tasks and mental health detection. The model outperforms several advanced models, yielding a notable 20.7% improvement over GPT-3.5-turbo and a 6.6% improvement over Llama-3-8b.
3. Self-Play Training: By assuming dual roles of patient and therapist, the model generates a wealth of diagnostic, treatment, and medication data through iterative self-play. This process not only refines the existing model capabilities but also enhances the personalization aspect of AI-driven mental health support.

Numerical Results and Model Performance

The experimental results demonstrate significant advancements: MentalArena outperforms prominent models such as GPT-4o across multiple tasks. The fine-tuned models showed substantial gains in accuracy, especially in biomedical QA and mental health detection tasks. Notably, the framework maintains the general performance of models across other standard benchmarks, reducing concerns about catastrophic forgetting.

Implications and Future Directions

The implications of this work are substantial, as it provides a cost-effective and efficient means to advance personalized mental health care through AI. The ability of MentalArena to simulate patient-therapist interactions opens new avenues for research in adaptive treatment strategies and may inspire further exploration in modeling complex human behaviors.

Looking forward, the paper suggests potential applications in general medical domains, demonstrating the framework's versatility. Future research could focus on extending the self-play paradigm to other areas of healthcare, possibly incorporating more sophisticated multi-modal data to enrich the training process.

Conclusion

MentalArena's innovative self-play framework provides a path forward in the AI-driven diagnosis and treatment of mental health disorders. By addressing key challenges like data scarcity and intent bias, the paper contributes valuable insights to the field, promising enhanced accessibility to personalized mental health care. The robustness and adaptability of the framework mark a significant step in the intersection of AI and mental health, paving the way for further advancements.

This comprehensive approach not only augments existing models with domain-specific expertise but also sets a precedent for the integration of AI in sensitive healthcare applications, ensuring a thoughtful balance between technical progress and ethical considerations.