Summary of Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory, by Kunyao Lan et al.
Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory
by Kunyao Lan, Bingrui Jin, Zichen Zhu, Siyuan Chen, Shu Zhang, Kenny Q. Zhu, Mengyue Wu
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 This research introduces Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. The AMC system consists of a tertiary memory structure, dialogue control and reflect plugin, and memory sampling module, leveraging the skills reflected by the psychiatrist agent to achieve great accuracy on depression risk and suicide risk diagnosis via conversation. Experimental results demonstrate that the system can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying their weights, even when only a few representative labeled cases are available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better ways to diagnose depression using computers. It introduces a new type of AI called Agent Mental Clinic (AMC) that can have conversations with people and help diagnose depression. The AMC system uses special structures like memory and control systems to make it more accurate. This means the AI can learn from doctors and other experts, even if there’s only a little information available. It’s a new way to use computers to help diagnose mental health problems. |
Keywords
» Artificial intelligence » Optimization
