Analogical reasoning is considered core to human learning and cognition.
Recent studies have compared the analogical reasoning abilities of human
subjects and Large Language Models (LLMs) on abstract symbol manipulation
tasks, such as letter string analogies. However, these studies largely neglect
analogical reasoning over semantically meaningful symbols, such as natural
language words. This ability to draw analogies that link language to
non-linguistic domains, which we term semantic structure-mapping, is thought to
play a crucial role in language acquisition and broader cognitive development.
We test human subjects and LLMs on analogical reasoning tasks that require the
transfer of semantic structure and content from one domain to another. Advanced
LLMs match human performance across many task variations. However, humans and
LLMs respond differently to certain task variations and semantic distractors.
Overall, our data suggest that LLMs are approaching human-level performance on
these important cognitive tasks, but are not yet entirely human like.

人类学习和认知中的类比推理被认为是核心。最近的研究比较了人类与大型语言模型（LLMs）在抽象符号操作任务（如字母串类比）上的类比推理能力。然而，这些研究在对语义有意义的符号（如自然语言词汇）进行类比推理时基本上被忽略了。这种将语言与非语言领域相关联的类比能力，我们称之为语义结构映射，被认为在语言习得和更广泛的认知发展中起着关键作用。我们在要求从一个领域向另一个领域转移语义结构和内容的类比推理任务上对人类与 LLMs 进行了测试。先进的 LLMs 在许多任务变体上与人类表现相匹配。然而，人类和 LLMs 对某些任务变体和语义干扰有不同的反应。总的来说，我们的数据表明 LLMs 在这些重要的认知任务上正在接近人类水平的表现，但还不完全接近人类。