With no restriction on what an actual word may look like, high-frequency patterns of any kind -including individual segments and short n-gram like sequences of segments -can be employed by the models to explain the input they get.
for little input with overall few word tokens and, consequently, relatively few repetitions for each of the actual word types, the evidence for high-frequency non-words is extremely high, leading to over-rather than undersegmentation.