揭开神秘的链条,树和思想图表.
IEEE transactions on pattern analysis and machine intelligence
|August 12, 2025
概括
本研究介绍了结构增强的大型语言模型 (LLM) 推理的总体蓝图,分类提示技术,如思维链和思想图表,以提高复杂任务上的LLM性能.
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.
背景情况:
- 大型语言模型 (LLM) 显示出显著的进展,即时工程增强了它们的功能.
- 结构增强的提示,包括思维链和思维图,指导LLM推理,以提高任务性能.
研究的目的:
- 为有效和高效的LLM推理方案设计一个一般的蓝图.
- 建立结构增强的LLM推理的第一个分类,分析结构,表示和算法.
主要方法:
- 对即时执行管道的深入分析.
- 为结构增强的LLM推理方案开发分类法,称为"推理拓".
- 使用拟议分类法对现有的提示方案进行比较.
主要成果:
- 为LLM推理方案提出了一个一般的蓝图.
- 建立了结构增强的LLM推理的第一个分类,详细说明推理拓.
- 分析揭示了设计选择如何影响性能和成本.
结论:
- 该研究为理解和推进结构增强的LLM推理提供了一个框架.
- 这项工作澄清了概念,对现有方法进行了分类,并确定了快速工程的未来研究挑战.
- 拟议的分类学有助于开发更有效和高效的LLM推理技术.
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