使用混沌模糊逻辑增强的大型语言模型增强教育问答系统
Haoyuan Chen1, Nuobei Shi1,2, Ling Chen3
1Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
本研究介绍了CHAQS,这是一个定制的大型语言模型 (LLM) 用于教育问答. CHAQS提高了精度和回忆,增强了智能问答系统.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 自然语言处理自然语言处理.
背景情况:
- 在线问答平台需要大量的人力支持.
- 开发用于教育的智能问答系统面临着独特的挑战.
研究的目的:
- 提出一种新的定制大型语言模型 (LLM),命名为基于混乱的LLM的教育问答系统 (CHAQS).
- 为教育部门增强智能问答系统.
主要方法:
- 利用了超过383,000对的教育数据集.
- 在ChatGLM基线模型上采用了微调技术,包括p调整v2,低级调整 (LRA) 和参数结.
- 集成的模糊逻辑和李振荡器用于参数调节和响应改进.
主要成果:
- 在精度方面取得了5.12%的改进.
- 增加了11%的召回.
- 与其他车型相比,F1得分提高了8%.
结论:
- 查克斯 (CHAQS) 方法学显著提高了教育问答系统的性能.
- 将先进的调整技术与模糊逻辑相结合,可以提高模型的精度和适应性.
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