研究和改进人类和机器的推理
Nicolas Yax1,2,3, Hernán Anlló1,2, Stefano Palminteri4,5
1Laboratoire de neurosciences cognitives et computationnelles, Institut national de la santé et de la recherche médicale, Paris, France.
Communications psychology
|September 6, 2024
概括
这项研究比较了使用认知心理学实验的大型语言模型 (LLM) 和人类的推理. 虽然早期的LLM显示出类似人类的错误,但新的模型显示出更好的推理,尽管人类和AI对提示的反应不同.
科学领域:
- 认知心理学 认知心理学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 认知心理学广泛研究人类的推理和决策,通常侧重于有限的理性.
- 大型语言模型 (LLM) 是越来越复杂的AI系统,其推理能力具有显著的兴趣.
- 将人工智能和人类认知进行比较,可以提供对这两个领域的见解.
研究的目的:
- 在大型语言模型 (LLM) 和人类中调查和比较推理过程.
- 利用认知心理学工具来评估人类和LLMs的有限理性.
- 为了确定人类和LLMs之间的推理错误和表现的相似之处和差异.
主要方法:
- 向人类参与者和各种预先训练的LLMs进行了经典认知实验的新型变体.
- 交叉比较了人类参与者和LLM在这些认知任务上的表现.
- 分析推理错误和对不同提示策略的响应模式.
主要成果:
- 大多数评估的LLM表现出类似于基于启发式的人类推理的推理错误.
- 尽管表面上有相似之处,但在人类和LLM推理之间观察到显著差异.
- 最近的LLM版本显示,与早期模型相比,推理限制大大减少.
- 人类和LLM对旨在提高绩效的特定提示策略的反应有所差异.
结论:
- 虽然LLM可以模仿某些人类推理错误,但它们的潜在认知过程可能不同.
- 法学士技术的进步正在导致更强大的推理能力.
- 了解人与人工智能交互和提示的细微差别对于人工智能开发和认知科学都至关重要.
- 该研究强调了比较人类和机器认知的认识学挑战和影响.
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