In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2‐year‐olds seem to intuit the whole range of things in a category from hearing a single instance named—they have word‐learning biases. This is not the case for children with relatively small vocabularies (late talkers). We present a computational model that accounts for the emergence of word‐learning biases in children at both ends of the vocabulary spectrum based solely on vocabulary structure. The results of Experiment 1 show that late‐talkers' and early‐talkers' noun vocabularies have different structures and that neural networks trained on the vocabularies of individual late talkers acquire different word‐learning biases than those trained on early‐talker vocabularies. These models make novel predictions about the word‐learning biases in these two populations. Experiment 2 tests these predictions on late‐ and early‐talking toddlers in a novel noun generalization task.