大型语言模型是知识推理的上下文教师
Jiachen Zhao1, Zonghai Yao2, Zhichao Yang2
1Northeastern University.
概括
大型语言模型 (LLM) 可以作为有效的语境教学 (ICT) 教师,优于人类教师. 自我解释和反教方法通过调整教师和学生的模型解释来改善基于LLM的ICT.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 语境教学 (ICT) 依赖于人为的例子,这些例子是昂贵的和可变的.
- 大型语言模型 (LLM) 为创建上下文示范提供了一个潜在的替代方案.
研究的目的:
- 调查LLM是否可以在ICT中比人类更有效的教师.
- 开发新的方法来改进基于LLM的ICT.
主要方法:
- 提出自我解释:使用LLM的自我生成的解释作为背景演示.
- 验证编码特异性假设:教师示例应该与学生培训数据相匹配.
- 引入反向教学:将教师和学生的法学课程协调一致,以提高ICT绩效.
主要成果:
- "自我解释"的表现明显优于人为制造的范例和其他基线.
- 类似于学生LLM的自我解释的解释可以作为更好的演示.
- 回教使一个较小的LLM能够教授一个更大的LLM,在准确度上超越了人类教师.
结论:
- 与人类相比,LLM可以成为优秀的上下文教师.
- 提出的自我解释和反教方法增强了基于LLM的ICT.
- 教师和学生的LLM解释之间的协调对于有效的ICT至关重要.
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