对心理健康的大型语言模型的评估:从知识测试到疾病诊断
Yijun Xu1, Zhaoxi Fang1,2, Weinan Lin1
1Department of Computer Science and Engineering, Shaoxing University, Shaoxing, China.
Frontiers in psychiatry
|August 22, 2025
概括
这项研究评估了15种用于中国心理健康的大型语言模型. DeepSeek-R1,QwQ和GPT-4.1在知识测试和诊断方面表现出卓越的表现.
科学领域:
- 医疗保健中的人工智能
- 计算精神病学
- 数字心理健康
背景情况:
- 大型语言模型 (LLM) 在心理健康评估,辅导和教育中提供了新的应用.
- 在中国等特定文化背景下评估LLM的表现对于负责任的部署至关重要.
研究的目的:
- 系统地评估15个最先进的心理健康知识和诊断任务在中国的背景下.
- 确定领先的LLM,并提供对中国心理健康应用的模型选择和改进的见解.
主要方法:
- 评估了15个LLM,包括DeepSeek-R1,GPT-4.1,Llama4和QwQ.
- 使用公开可用的数据集:Dreaddit,SDCNL和CAS顾问资格考试问题.
- 对心理健康知识准确性和精神疾病诊断能力的评估.
主要成果:
- 与其他模型相比,DeepSeek-R1,QwQ和GPT-4.1表现出卓越的性能.
- 这些高性能模型在知识准确性和诊断准确性方面都表现出色.
- 确定了当前在中国心理健康领域的LLM的优点和局限性.
结论:
- DeepSeek-R1,QwQ和GPT-4.1对于中国的心理健康应用来说是有前途的.
- 这些研究结果为选择和增强中国敏感心理健康任务的LLM提供了指导.
- 需要进一步的研究来解决这个领域的局限性和优化LLM的实用性.
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