从系统1到系统2:对推理大型语言模型的调查
IEEE transactions on pattern analysis and machine intelligence
|November 25, 2025
概括
这项调查探讨了推理的大型语言模型 (LLM),这些模型将直观的系统1和故意的系统2思维相结合. 这些高级LLM在数学和编码等复杂任务中表现出类似人类的认知能力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 基础的大型语言模型 (LLM) 在快速决策方面表现出色,但缺乏深层次的,类似系统2的推理.
- 人类智能依赖于从直观的系统1过渡到故意的系统2思维,以获得准确性和减少偏见.
研究的目的:
- 调查推理的大型语言模型 (LLM) 的发展和能力.
- 探索基础的LLMs和系统2概念如何结合起来,创建先进的推理模型.
- 为了提供一个全面的概述,推理LLM建设,演变和评估.
主要方法:
- 概述基础的LLM进展和系统2的发展.
- 分析推理的LLM建设和演变.
- 检查核心推理方法和基准.
- 代表性推理LLM表现的比较.
主要成果:
- 推理LLM在数学和编码方面表现出专家水平的表现.
- 这些模型模仿了故意的系统2推理,展示了类似人类的认知能力.
- 在构建和评估高级推理法学士课程方面取得了重大进展.
结论:
- 推理的LLM代表了迈向人类水平智能的重要一步.
- 预计LLM推理的进一步进步将受到持续的研究和开发的推动.
- 这项调查是人工智能推理领域快速发展的创新资源.
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