MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents
<|reference_start|>MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents: The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is promising to boost the AI mental healthcare community. However, directly collecting the conversations in real diagnosis scenarios is near impossible due to stringent privacy and ethical considerations. To address this issue, we seek to synthesize diagnostic conversation by exploiting anonymous patient cases that are easier to access. Specifically, we design a neuro-symbolic multi-agent framework for synthesizing the diagnostic conversation of mental disorders with large language models. It takes patient case as input and is capable of generating multiple diverse conversations with one single patient case. The framework basically involves the interaction between a doctor agent and a patient agent, and achieves text generation under symbolic control via a dynamic diagnosis tree from a tool agent. By applying the proposed framework, we develop the largest Chinese mental disorders diagnosis dataset MDD-5k, which is built upon 1000 cleaned real patient cases by cooperating with a pioneering psychiatric hospital, and contains 5000 high-quality long conversations with diagnosis results as labels. To the best of our knowledge, it's also the first labelled Chinese mental disorders diagnosis dataset. Human evaluation demonstrates the proposed MDD-5k dataset successfully simulates human-like diagnostic process of mental disorders. The dataset and code will become publicly accessible in https://github.com/lemonsis/MDD-5k.<|reference_end|>
@article{yin2024mdd-5k:,
    title={MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders
  Synthesized via Neuro-Symbolic LLM Agents},
    author={Congchi Yin, Feng Li, Shu Zhang, Zike Wang, Jun Shao, Piji Li, Jianhua
  Chen, Xun Jiang},
    journal={arXiv preprint arXiv:2408.12142},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12142},
    primaryClass={cs.CL cs.AI}
}