  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.
