人类与大型语言模型中的归纳推理
Inductive reasoning in humans and large language models
AI总结：
本研究通过将GPT-3.5和GPT-4应用于人类属性归纳任务，发现GPT-4在多数情况下能定性匹配人类表现，并提供了两个用于人机智能比较的大型基准数据集。
摘要：
大型语言模型近期令人瞩目的表现，让许多人开始思考它们在多大程度上可以作为通用智能模型，或者是否与人类认知相似。我们通过将GPT-3.5和GPT-4应用于人类归纳推理中的一个经典问题（即属性归纳）来探讨这一问题。在两项实验中，我们收集了人类在跨越多个领域的一系列属性归纳任务上的判断。尽管GPT-3.5难以捕捉人类行为的许多方面，但GPT-4却成功得多：在大多数情况下，其表现在定性上与人类相匹配，唯一显著的例外是它未能捕捉到前提非单调性现象。我们的工作表明，属性归纳允许对人类与机器智能进行有趣的比较，并提供了两个大型数据集，可作为未来此类研究的基准。
英文摘要：
The impressive recent performance of large language models has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3.5 and GPT-4 to a classic problem in human inductive reasoning known as property induction. Over two experiments, we elicit human judgments on a range of property induction tasks spanning multiple domains. Although GPT-3.5 struggles to capture many aspects of human behaviour, GPT-4 is much more successful: for the most part, its performance qualitatively matches that of humans, and the only notable exception is its failure to capture the phenomenon of premise non-monotonicity. Our work demonstrates that property induction allows for interesting comparisons between human and machine intelligence and provides two large datasets that can serve as benchmarks for future work in this vein.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
