Summary of Llms Can Simulate Standardized Patients Via Agent Coevolution, by Zhuoyun Du et al.
LLMs Can Simulate Standardized Patients via Agent Coevolution
by Zhuoyun Du, Lujie Zheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haohao Ying
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
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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 proposed EvoPatient framework leverages Large Language Models (LLMs) to simulate medical consultations between patient agents and doctor agents, enabling human doctor training. By iteratively improving the quality of questions and answers through multi-turn dialogues, EvoPatient outperforms existing reasoning methods in requirement alignment by over 10% and achieves a balance between resource consumption and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical professionals need to be trained using standardized patients (SPs), which is a complex challenge. Current research focuses on improving data retrieval accuracy or adjusting prompts through human feedback, but neglects the importance of patient agents learning standard presentation patterns. EvoPatient addresses this gap by proposing a simulated patient framework that simulates diagnostic processes through dialogues and improves questions and answers over time. |
Keywords
» Artificial intelligence » Alignment
