基于卡尔曼的自适应时刻估计优化算法来增强LLM中的GPT,用于对患者健康相关反的医疗情绪分析
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
一个新的KAdam-EnGPT4LLM优化算法改进了用于医疗情绪分析的大型语言模型 (LLM). 这提高了培训效率,并降低了在医疗保健中分析患者反的成本.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 大型语言模型 (LLM) 从患者反中进行了先进的医疗情绪分析.
- 高的培训成本和数据要求限制了LLM在现实世界医疗保健中的应用.
研究的目的:
- 引入KAdam-EnGPT4LLM,这是一个优化算法,用于高效的LLM培训.
- 开发GPT4ALL-MediSentAly-KAdam以改善医疗情绪分析.
主要方法:
- 拟议的KAdam-EnGPT4LLM,结合了卡尔曼过器和自适应时刻估计.
- 应用KAdam-EnGPT4LLM来训练GPT4ALL进行医疗情绪分析,从而创建了GPT4ALL-MediSentAly-KAdam.
- 在医疗问题和答案数据集上评估性能.
主要成果:
- GPT4ALL-MediSentAly-KAdam表现出更快的融合和更稳定的培训.
- 与传统的LLM相比,实现了卓越的准确性,回忆,F1得分和运行时间.
- 超过了基线模型的表现,包括GPT4ALL,Ada,Babbage,Curie和Duvinci.
结论:
- KAdam-EnGPT4LLM显著提高了LLM培训效率,并降低了医疗情绪分析的成本.
- GPT4ALL-MediSentAly-KAdam提供了一种更有效的解决方案,用于分析医疗保健中的患者反.
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