Summary of Mdd-5k: a New Diagnostic Conversation Dataset For Mental Disorders Synthesized Via Neuro-symbolic Llm Agents, by Congchi Yin et al.
MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents
by Congchi Yin, Feng Li, Shu Zhang, Zike Wang, Jun Shao, Piji Li, Jianhua Chen, Xun Jiang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The neuro-symbolic multi-agent framework designed in this paper aims to synthesize diagnostic conversations for mental disorders by leveraging large language models and anonymized patient cases. The framework involves an interaction between a doctor agent and a patient agent, generating diverse conversations under symbolic control via a dynamic diagnosis tree. By applying the proposed framework, the largest Chinese mental disorders diagnosis dataset MDD-5k is developed, comprising 5000 high-quality long conversations with diagnosis results and treatment opinions as labels. This dataset is built upon 1000 real, anonymed patient cases in cooperation with Shanghai Mental Health Center. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a synthetic diagnostic conversation dataset for mental disorders. It’s hard to collect these conversations because they’re private and need to follow strict rules. To solve this problem, the researchers use old patient cases that are easier to get and design a special computer program to make fake conversations with those cases. The program has two parts: a doctor and a patient. They work together to make many different conversations using a special tree-like structure. By doing this, they made the biggest dataset for diagnosing mental disorders in China, which is also labeled correctly. This means it can be used to train machines to help diagnose these disorders. |
