Induction-the ability to generalize from existing knowledge-is the cornerstone of intelligence. Cognitive models of human induction are largely limited to toy problems and cannot make quantitative predictions for the thousands of different induction arguments that have been studied by researchers, or to the countless induction arguments that could be encountered in everyday life. Leading large language models (LLMs) go beyond toy problems but fail to mimic observed patterns of human induction. In this article, we combine rich knowledge representations obtained from LLMs with theories of human inductive reasoning developed by cognitive psychologists. We show that this integrative approach can capture several benchmark empirical findings on human induction and generate human-like responses to natural language arguments with thousands of common categories and properties. These findings shed light on the cognitive mechanisms at play in human induction and show how existing theories in psychology and cognitive science can be integrated with new methods in artificial intelligence, to successfully model high-level human cognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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头脑和机器中的归纳推理。


归纳——从现有知识中概括的能力——是智能的基石。人类归纳的认知模型在很大程度上局限于玩具问题，无法对研究人员研究的数千种不同的归纳论点或日常生活中可能遇到的无数归纳论点做出定量预测。领先的大型语言模型 （LLMs） 超越了玩具问题，但未能模拟观察到的人类诱导模式。在本文中，我们将从 LLMs认知心理学家开发的人类归纳推理理论相结合。我们表明，这种综合方法可以捕获关于人类归纳的几个基准实证发现，并对具有数千个常见类别和属性的自然语言论点产生类似人类的响应。这些发现阐明了在人类诱导中发挥作用的认知机制，并展示了心理学和认知科学的现有理论如何与人工智能中的新方法相结合，以成功模拟高级人类认知。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。