Do large language models solve verbal analogies like children do?
Claire E. Stevenson Mathilde ter Veen Rochelle Choenni Han L. J. van der Maas Ekaterina Shutova
Analogy-making lies at the heart of human cognition. Adults solve analogies
such as \textit{Horse belongs to stable like chicken belongs to ...?} by
mapping relations (\textit{kept in}) and answering \textit{chicken coop}. In
contrast, children often use association, e.g., answering \textit{egg}. This
paper investigates whether large language models (LLMs) solve verbal analogies
in A:B::C:? form using associations, similar to what children do. We use verbal
analogies extracted from an online adaptive learning environment, where 14,002
7-12 year-olds from the Netherlands solved 622 analogies in Dutch. The six
tested Dutch monolingual and multilingual LLMs performed around the same level
as children, with MGPT performing worst, around the 7-year-old level, and XLM-V
and GPT-3 the best, slightly above the 11-year-old level. However, when we
control for associative processes this picture changes and each model's
performance level drops 1-2 years. Further experiments demonstrate that
associative processes often underlie correctly solved analogies. We conclude
that the LLMs we tested indeed tend to solve verbal analogies by association
with C like children do.